Evaluation of Water Network Construction Effect Based on Game-Weighting Matter-Element Cloud Model

Abstract

Water network construction is one of the important ways to solve complex water problems at present. It is crucial for the optimal allocation of water resources, flood control, disaster reduction, protection of water ecology, water security, and sustainable urban development. Accordingly, this study formulates an index system for assessing the efficacy of water network construction based on the Driving Force–Pressure–State–Influence–Response (DPSIR) model, taking into account the four dimensions of optimal allocation of water resources, flood control and disaster reduction in river basins, protection of water ecosystems, and intelligent water network management. The proposed index system comprises four key aspects, which are utilized to evaluate the effectiveness of water network construction efforts. Then, the game-weighting method and the matter-element extension method improved by the cloud theory established an evaluation model to evaluate and compare the water network construction effects of the two cities in Henan Province. Finally, the GM (1,1) model was used to evaluate the water network construction effects, and future trends were predicted. The results show the following: (1) On the whole, the effect of water network construction in the two cities is constantly improving; (2) There has been a significant improvement in the intelligent management of water networks. The main reason for this result is that the “Internet +” has promoted the intelligent construction of water networks; (3) The water ecological environment, flood control, and drainage capabilities continued to improve, which has largely guaranteed the basic security bottom line of urban development space; (4) The advancement and utilization of water resources has undergone gradual improvements over time, with key impact metrics centered on water supply safety factors and the development and utilization of water resources. As water supply sources continue to diversify, it is expected that the aforementioned situation will be ameliorated in the future; (5) The predicted value shows that the water network construction of the two cities can basically meet the planned value of each index when the water network construction reaches the planning level. This paper provides help to promote the sustainable use of water resources and ensure the sustainable development of cities.

1. Introduction

Water is an important basis for the development of human society, not only the source of life but also the basis of production and ecology. However, the spatial distribution of water resources is highly unequal, resulting in an imbalance between water supply and demand in many countries [1]. At the same time, due to climate change, the uneven distribution of water resources has exacerbated the severity of floods and droughts [2], which has brought greater challenges to the rational use and management of water resources. Of the 17 Sustainable Development Goals (SDGs) announced by the United Nations General Assembly, at least four are related to the sustainable use and management of water resources [3]. According to the “2020 China Flood and Drought Disaster Prevention Bulletin”, from 2010 to 2020, a total of 7986 people died in floods in China, and the direct economic loss was 2610.6 billion RMB [4]. Therefore, realizing the sustainable use and management of water resources is a necessary condition for the development of today’s society.

In order to ensure the rational allocation of water resources, it is necessary to maintain, reshape, or construct natural water system channels through both natural and artificial driving forces so as to ensure the rational allocation of water resources. This approach aligns with current and future socioeconomic development [5]. However, with the development of the economy and society today, the contradiction between people and water has undergone profound changes. The thinking about water control has also undergone profound changes. Under the premise of ensuring the safety of rivers, water conservancy construction has paid more attention to water ecological protection and restoration [6], water resource conservation [7], and water environmental pollution prevention and control [8]. In this context, the construction and operation of the water network was born. As a complex system engineering, the water networks are capable of integrating the functions of overall water resource allocation, water and drought disaster prevention and control, and water ecological protection. It not only optimizes and adjusts the patterns of river and lake water systems, realizes the adjustment of water resources, and maintains the ecological security of rivers and lakes but also promotes the coordinated and sustainable development of the economic society and the ecological environment [9].

In recent years, many countries worldwide have begun to pay attention to the planning, construction, and key technology research of national water networks and regional water networks. Israel has established a national water network and integrated the country’s surface water, groundwater, seawater, and other water resources into the national water network for unified deployment; the United States has built a national smart water network (NSWG) with the Colorado River and Mississippi River connectivity projects as the backbone, successfully solving the drought problem in the West; EU countries, such as Spain and France, also promoted the construction of smart water networks in their countries through top-level planning, key technology research and development, and demonstration project construction [10]. Similarly, the operation and effectiveness of Internet of Things technologies also provide technical support for the construction of water networks, connecting sensors to monitor real-time parameters, such as water flow, water quality, and pressure [11]. Remote monitoring and operation systems enable remote monitoring and control of water network facilities. They collect a large amount of real-time data and process it through data analysis algorithms to provide insights into the operational status and trends of the water supply network [12]. China’s water administration departments and scholars have successively proposed plans and assumptions for establishing a national water network hub, integrated water network, and modern water network, carried out practical explorations of urban-scale smart water networks and smart water affairs, and proposed to rebuild river concepts and methods of lake dynamic connection and restoration of water network ecological environment.

Although the concept of water network construction has been widely known, the standard of water network construction effect has not been established, and there is a lack of a standard system and decision-making system that can be used in practice. The primary objective of water network construction is to achieve overall water resource allocation. It is crucial to ensure the prevention and control of water and drought disasters as well as water ecological security while also guaranteeing the balance of water resource allocation. In the context of coordinating national security and high-quality regional development, the evaluation of water network construction effects should not only consider the ability to optimize the allocation of water resources and improve the natural ecological environment but also require it to have a strong public interest and meet the requirements of the public. At the same time, water network construction must be able to promote sustainable development of society, economy, and environment.

In the current study, Qiao and Chen constructed a water security evaluation model considering the Water Disaster Risk Index (WDRI), Water Environment Risk Index (WERI), and water supply demand [9]. Balaei et al. evaluated water supply resilience from the perspectives of vulnerability, social capital, organizational capacity, and economic capital [13]. Cao et al. combined water quantity and water quality to evaluate water resource protection and utilization with a new linear additive index [14]. Sun et al. built a comprehensive evaluation model for the sustainability of water resources systems based on the Analytic Hierarchy Process (AHP) [15]. Tang et al. selected eight water quality indicators to evaluate water quality risks in aquatic ecology [16]. However, scholars in these studies often focus on a single study of water disasters, water resources, water ecology, and water environment. In order to construct an evaluation system for water network construction more comprehensively, the DPSIR framework is introduced, which is a framework that can construct complex environmental problems and unify with social and natural sciences [17]. The DPSIR model has been widely used in different fields [18], including air quality assessment [19], irrigation water efficiency assessment [20], and regional water resource sustainability assessment [15]. The evaluation of the effect of water network construction involves multi-dimensional sustainability and needs to be evaluated from the point of view of the aspects of optimal allocation of water resources, natural ecological environment, flood control and disaster reduction in river basins, water ecological protection, and smart water conservancy.

Due to the uncertainty and ambiguity of the indicators involved in the evaluation of the multi-dimensional water network construction effect, in order to overcome the problem of information distortion and loss in the evaluation process, existing research mainly includes T-S fuzzy neural network method [21], functional gray relation (FGR) [22], system dynamics Combined with AHP [23], gray target theory analysis [24], and other methods. The above methods have specific advantages and disadvantages, but the data in the evaluation of water network infrastructure construction [25] effects are not fixed values but fluctuate within a certain range, which is uncertain and also requires the combination of qualitative concepts and quantitative indicators. The cloud model [26] proposed by Li Deyi based on the traditional fuzzy set theory and conceptual statistics theory can effectively solve the problems of uncertainty and ambiguity [27], and it is widely used in the evaluation of renewable energy [28], the health status of water cycle evaluation [29], comprehensive evaluation of water resources carrying capacity [21], ecological environment vulnerability evaluation [30], and other fields. In view of the above problems, this paper uses the game-weighting matter-element cloud model to balance the subjectivity and objectivity of indicator weights and solve the uncertainty and ambiguity of indicators in the evaluation process.

The main steps of this research are as follows: (1) From the four dimensions of optimal allocation of water resources, flood control and disaster reduction in river basins, protection of water ecosystems, and intelligent water network management, construct an evaluation index system for water network construction effects based on the DPSIR model; (2) An evaluation method combining game theory combination weighting and matter-element cloud model algorithm is proposed to evaluate the effect of water network construction; (3) Based on the GM (1,1) prediction model, predict the effect of water network construction in two areas of Henan Province, China, and provide a reference for the realization of water network construction.

2. Study Area and Method

2.1. Study Area

Xinyang City and Pingdingshan City are currently two pilot cities located in Henan Province, China, that have completed water network construction. This paper takes these two cities as examples to analyze the impact of water network construction on local economic and social development and then compare and analyze the effects of the difference between the two constructions.

Xinyang is located in the southernmost part of Henan Province, between the northern foot of the Dabie Mountains and the Huaihe River. The geographical coordinates are 113°45′~115°55′ east longitude and 30°23′~32°27′ north latitude. The whole territory is 205 km long from east to west, 142 km wide from north to south, and covers an area of 18,900 km². It belongs to two major river basins, the Yangtze River and the Huaihe River. Among them, the Huaihe River basin accounts for 98.2% of the total area of the city. The area has abundant rainfall, with an average annual precipitation of 1116 mm. The distribution of precipitation is uneven within a year, and the inter-annual variation is also large. The maximum annual precipitation is as high as 1734 mm; the annual minimum precipitation is only 637 mm, and the ratio of abundance to drought is nearly three times The annual average water surface evaporation is about 800–1000 mm.

Pingdingshan is located in the central part of Henan Province. The geographical coordinates are between 33°08′–34°20′ north latitude and 112°14′–113°41′ east longitude. Pingdingshan City belongs to the upper reaches of the Huaihe River Basin and is divided into two water systems, the Hongru River and the Shaying River. The drainage area of the Shaying River system within the area is 7293 km², accounting for 93.53% of the city’s jurisdiction area. The annual average precipitation in the area is 821.1 mm, and it increases from north to south. The western mountainous area is larger than the eastern plain. For the same area, the distribution of precipitation in a year is uneven. The precipitation from June to September accounts for 60% to 80% of the annual precipitation and the precipitation is relatively concentrated. The annual evaporation is generally between 863.4 and 1038.7 mm, and the evaporation from May to August accounts for about 45% of the whole year.

In recent years, Xinyang City and Pingdingshan City have been pioneers in water network construction in Henan Province. To better utilize the ecological foundation of the region, which features abundant mountains, waters, and greenery, and achieve high-quality development, Xinyang City has been dedicated to building a sustainable ecological water network based on the interlacing rivers, canals, and a wide range of lakes, wetlands, and ponds in the region, featuring “multiple source allocation and multiple-cycle utilization”. Meanwhile, Pingdingshan City has been committed to constructing a “four-dimensional connected and three-dimensional circulation” ecological water network to address issues, such as insufficient water resources, insufficient water quantity, poor water quality, and low water governance capacity, thereby enhancing water security capabilities. The modern water conservancy infrastructure network features “a comprehensive system, abundant water in wet and dry seasons, smooth circulation, multiple sources complementation, high safety and efficiency, and clean water and green shores”. The specific locations are shown in Figure 1.

2.2. DPSIR Mode

Driving Force–Pressure–State–Influence–Response (DPSIR) can systematically analyze the relationship between economic benefits and the impact on the ecological environment and has the characteristics of comprehensiveness, systematicity, integrity, and flexibility [31]. In this framework, driving force refers to human activities and processes [32]; pressure refers to social behavior and development that have a direct impact on the environment, economy, and society [33]; state reflects the status of the environment and natural resources, etc. [34]; impact refers to the measurement of environmental impact [35]; response refers to specific actions to reduce stress and impact [36]. This model has been popularized by scholars for ecological effect evaluation [37,38,39], water resource security evaluation [40,41], water environment coordination [42,43], and tourism ecological health evaluation [44,45]. The specific logic structure is shown in Figure 2 below.

Since water network construction is a comprehensive water conservancy project, the DPSIR model can well reflect the relationship between economic, social, environmental, and resource elements in water network construction. In the urban water network, the driving force is the potential cause of the change in the urban water network, generally including natural driving force, human driving force, and social driving force; pressure refers to the pressure brought to the urban water network under the action of the driving force. It usually refers to the impact of the driving force of the water network on the surrounding natural resources and ecological environment; the impact refers to the state of the urban water network under the action of the driving force, pressure, and the possible impact; Response refers to the analysis of the state and impact of urban water networks under driving forces and pressures, and proposing corresponding response measures to improve and enhance the efficiency of water networks. Selecting the DPSIR model to construct the water network construction effect evaluation index system can more comprehensively reflect the water network effect status, more clearly express the relationship between indicators in the water network effect system framework, and more intuitively judge the water network effect status.

2.3. Construction of Index System

This paper mainly focuses on the DPSIR model, combined with water network construction as a carrier, considering the functions of optimal allocation of water resources, flood control and disaster reduction in river basins, protection of water ecosystems, and referring to existing research results and documents, such as standards and technical guidelines issued by management departments. Constraint conditions establish 26 quantitative indicators and finally obtain an index system for urban water network construction effect evaluation, as shown in

Table 1. Construction and interpretation of indicator system.

Standard Layer	Index Layer	Explain
Driving force	D1: Control degree of water consumption target per 10,000 RMB of GDP [46]	The ratio of water consumption per ten thousand RMB of GDP to assessment value
	D2: Development and utilization of water resources [47]	The ratio of water supply to total water resources
	D3: Effective use coefficient of farmland irrigation water [46]	The ratio of the actual effective use of water to the total amount of water inflow in the irrigated area, excluding deep seepage and field loss
	D4: Water quality compliance rate of water function zone [48]	The ratio of the number of water quality up-to-standard water function areas to the total number of water function areas
	D5: Target control degree of water consumption per 10,000 RMB of industrial added value [46]	The ratio of water consumption per 10,000 RMB of industrial added value to assessment value
	D6: Water quality compliance rate of drinking water sources [46]	The ratio of the number of drinking water sources meeting the water quality standards to the total number of drinking water sources
Pressure	P1: Satisfaction rate of ecological flow in control sections of important rivers and lakes [49]	The compliance rate of the ecological flow targets determined by the main control sections in important rivers and lakes
	P2: Level 1~5 embankment compliance rate [50]	The ratio of the length of dikes of grades 1 to 5 reaching the flood control standard to the total length of dikes of grades 1 to 5
	P3: Elimination ratio of black and odorous water bodies in urban built-up areas [51]	The ratio of the number of black and odorous water bodies eliminated in urban built-up areas to the total number of water bodies
	P4: Ecological embankment ratio [52]	The ratio of ecological embankment to total embankment length
State	S1: Water shortage rate [53]	The gap between supply and demand as a percentage of water supply
	S2: Total water consumption control compliance [46]	Comparison of water allocation and total water consumption control indicators
	S3: Agricultural irrigation water metering [54]	The proportion of agricultural irrigation water intakes using metering facilities
	S4: Domestic and industrial water metering rates [46]	The ratio of automatic collection sites to total collection sites
	S5: Vertical connectivity of the river [55]	Evaluation of the number of buildings or facilities affecting river connectivity within a unit river length
Influence	I1: Water supply safety factor [56]	The ratio of effective water supply capacity to water supply volume
	I2: Urban water surface rate [52]	The ratio of urban water area to the total area of the region, indicating the development of urban water network area
	I3: The degree of safety and stability of the river bank and river bed [56]	It shows that the local, temporary, and relative variation range shown by the river is generally expressed by the stability coefficient as the water and sediment conditions of the river basin change over time
	I4: Average annual loss rate of flood disasters [57]	The ratio of the loss value of various properties or crops in the disaster-affected area to the pre-disaster value or normal value
	I5: Storage volume siltation loss rate [57]	Total siltation loss storage capacity as a percentage of total storage capacity
	I6: Compliance rate of waterlogging prevention and control [58]	The ratio of the area meeting the waterlogging prevention and control standard to the total area of the built-up area
Response	R1: Disaster prevention and emergency plan formulation rate [58]	Implementation of non-engineering measures for disaster prevention and mitigation
	R2: Coverage of important river and lake monitoring stations [59]	The proportion of important river and lake monitoring stations to total river and lake monitoring stations
	R3: Intelligent control rate of major water conservancy projects [59]	The ratio of the number of major water conservancies projects, such as large reservoirs, dykes of level 3 and above, and major water diversion projects to achieve intelligent control in the total number of projects
	R4: Implementation rate of the strictest water resource management [50]	Implementation of the most stringent water resource management system
	R5: Degree of digital development [59]	Application degree of digital technology in the water network system

.

2.4. Game Theory Portfolio Empowerment

The comprehensive evaluation of the water network construction effect involves multiple influencing factors, and there are various complex relationships among the factors. It belongs to the multi-objective evaluation problem, which can be quantitatively calculated and analyzed through index weighting. Based on the game theory, this paper comprehensively considers the relationship between indicators, taking into account both subjective (Analytic Hierarchy Process) and objective (Entropy Weight Method) weights, and realizes weight optimization [60]. The specific calculation steps are as follows:

Assuming that there are $n$ indicators, $k$ methods are used to obtain $k$ weight values, and the weight matrix obtained via each method is $w_i$, and the weight matrix obtained via each method is linearly combined and optimized to obtain the minimum optimal value, as shown in Formula (1).

$$MIN={\Vert {\displaystyle {\sum }_{j=1}^n{a}_j{w}_i^T}\Vert }_2,i=1,2,\dots ,k$$

(1)

Take the first derivative of the matrix of Formula (1) and expand it to obtain

$$\left(\begin{array}{ccc}{w}_1{w}_1^T& \cdots & w_1{w}_k^T\\ ⋮& \ddots & ⋮\\ w_k{w}_1^T& \cdots & w_k{w}_k^T\end{array}\right)\left(\begin{array}{c}{a}_1\\ ⋮\\ a_k\end{array}\right)=\left(\begin{array}{c}{w}_1{w}_1^T\\ ⋮\\ w_k{w}_1^T\end{array}\right)$$

(2)

Using Formula (2) can be calculated $\left(a_1,a_2,\cdots a_k\right)$, and then this matrix is normalized to obtain the linear coefficient $a^{*}$:

$$a^{*}=\frac{a_k}{{\displaystyle {\sum }_k{a}_k}}$$

(3)

Finally, the combined weight matrix is obtained $w^{*}$:

$$w^{*}={\displaystyle \sum _{k=1}^k{a}^{*}{w}_k^T}$$

(4)

2.5. Matter-Element Cloud Model

Due to the fuzziness of certain data information and evaluation intervals in the evaluation of water network construction, the combination of the cloud model and matter-element extension model can fully leverage the advantages of the two models in analyzing and dealing with uncertainty, fuzziness, and randomness, achieving the mutual conversion of qualitative concepts and quantitative values [61] and determining the correlation degree of water network construction.

(1) 

Qualitative and quantitative transformation of cloud models

As the representation of matter element involves both the qualitative-to-quantitative and quantitative-to-qualitative processes, there is a certain degree of fuzziness and randomness in the representation. Therefore, the cloud model is used to handle the randomness and fuzziness of the matter element, as well as the correlation between randomness and fuzziness [62]. According to the sample analysis results, the fixed interval $\left[C_{\min },C_{\max }\right]$ of the characteristic value is used as the domain of discourse, and the three mathematical characteristics of the cloud model, expectation ($E_X$), entropy ($E_n$), and hyper-entropy ($He$), are calculated according to the following formula:

$$E_x=\frac{\left(C_{\max }+C_{\min }\right)}{2},E_n=\frac{\left(C_{\max }-C_{\min }\right)}{6},He=S$$

(5)

In the formula, $S$ is a constant, a value determined according to expert experience or practical problems. $He$ is an uncertain value, and its size determines the thickness of the cloud layer. The smaller the value, the thinner the cloud layer and the more blurred the correlation coefficient; the larger the value, the opposite is true.

(2) 

Matter-element theory

Matter-element theory is a theory based on artificial intelligence thinking and dealing with problems according to formal logic. It calculates through the correlation function composed of objects, object characteristics, and characteristic values, which can accurately reflect the relationship between quality and quantity [63]. It is mainly applied to how to deal with the contradictory nature of problems and represent the characteristics of things and the quantitative values of things related to those characteristics, all together. In the evaluation of matter-element, the commonly used matter-element correlation function $R=\left(N,c,v\right)$ is used, where $R$ is the basic element; $N$, $c$, and $v$ are matter elements, which represent the object, the features of the object, and the quantitative values of the object related to those features. When an object possesses non-single properties or features, a multidimensional matter element is used for representation [64].

$$R=\left[\begin{array}{ccc}N& c_1& v_1\\ & c_2& v_2\\ & \cdots & \cdots \\ & c_n& v_n\end{array}\right]=\left[\begin{array}{ccc}N& c_1& v_1\left(N\right)\\ & c_2& v_2\left(N\right)\\ & \cdots & \cdots \\ & c_n& c_n\left(N\right)\end{array}\right]=\left[\begin{array}{ccc}N& c_1& (E_1,H_1,H{e}_1)\\ & c_2& (E_2,H_2,H{e}_2)\\ & \cdots & \cdots \\ & c_n& (E_n,H_n,H{e}_n)\end{array}\right]$$

(6)

(3) 

Cloud correlation calculation

In this paper, the water network effect is used to evaluate the data in the grade boundary, and the normal distribution number $He$ is established with $E_x$ as the expectation and $E_n$ as the variance:

$$E_x=\frac{{\displaystyle \sum _{i=1}^n{x}_i}}{n},En=\sqrt{\frac{\pi }{2}}\times \frac{{\displaystyle \sum _{i=1}^n\left|x_i-E_x\right|}}{n},He=\sqrt{S^2-E_n{}^2}$$

(7)

Taking one index value $x$ as one cloud drop, calculate the degree of relevance in the domain of discourse by the following formula:

$$\lambda (x)=\exp \left[-\frac{{(x-E_x)}^2}{2{(E_n^{\prime })}^2}\right]$$

(8)

2.6. Comprehensive Evaluation of Water Network Construction Effect

Calculate the correlation degree of the index criterion layer according to the following formula, and the evaluation grade $i$ corresponding to ${\lambda }_i$ with the largest correlation degree is the evaluation grade of the corresponding object.

$${\lambda }_i={\displaystyle \sum _j\left({\omega }_j{\lambda }_{ij}\right)}$$

(9)

In the formula, ${\lambda }_i$ is the correlation degree of the first-level index to the evaluation level $i$; ${\omega }_j$ is the weight of the second-level index; ${\lambda }_{ij}$ is the correlation degree of the second-level index $j$ to the evaluation level $i$.

Calculate the correlation degree of the target layer according to the following formula:

$${\lambda }_{ij}={\displaystyle \sum _k\left({\omega }_{jk}{\lambda }_{ijk}\right)}$$

(10)

In the formula, ${\lambda }_{ijk}$ is the correlation degree between the second-level indicator corresponding to the $k$-th third-level indicator of $j$ and the evaluation level; ${\omega }_{jk}$ is the weight of the $k$-th third-level indicator corresponding to the second-level indicator $j$.

2.7. Model Data Prediction

GM(1,1) model transforms the original data into a series of data with obvious laws so as to establish a differential equation to reflect the internal connection and development law of things. Through the historical data of water network construction, to predict the future, compare it with the planning data, and propose improvement measures.

Firstly, the original sequence is established as $\left[X_1^0,X_2^0,X_3^0\right]$, and a new sequence $\left[X_1^1,X_2^1,X_3^1\right]$ is generated by one accumulation, where $X_k^1={\displaystyle \sum _{i=1}^k{X}_i^0},k=1,2,3$. Then, generate the mean sequence: $Z_k^1=a{X}_k^1+(1-a)X_{k-1}^1,k=2,3$, where $0\le a\le 1$ is the weight. Usually, the mean sequence $a=0.5$.

From this, the gray differential equation is established:

$$X_k^0+a{Z}_k^1=b,k=2,3$$

(11)

The corresponding $GM(1,1)$ whitening differential equation:

$$\frac{d{x}^1}{dt}+a{X}_t^1=b,k=2,3$$

(12)

Transpose the gray differential equation to obtain:

$$-a{\mathrm{Z}}_k^1+b=X_k^0,k=2,3$$

(13)

$a,b$ is an undetermined parameter, and the above formula can be written in the form of a matrix:

$$\left[\begin{array}{cc}-Z_2^1& 1\\ -Z_3^1& 2\end{array}\right]\left[\begin{array}{c}a\\ b\end{array}\right]=\left[\begin{array}{c}{X}_2^0\\ X_3^0\end{array}\right]$$

(14)

That is $X\beta =Y$. The estimated value of the parameter matrix $\beta$ can be determined by the method of least squares:

$$\hat{\beta }={(X^{T}X)}^{-1}{X}^{T}Y$$

(15)

From this, the estimated value of parameter $a,b$ is obtained, which is brought into the whitening equation to obtain the general solution of sequence $X_k^1$:

$$\widehat{X_k^1}=(X_1^0-\frac{b}{a})e^{-a(k-1)}+\frac{b}{a},k=2,3$$

(16)

Restore to the original sequence to obtain the prediction function:

$$\widehat{X_k^0}=(X_1^0-\frac{b}{a})e^{-a(k-1)}(1-e^a),k=2,3$$

(17)

2.8. Overall Research Design

The water network construction effect evaluation model in this paper is composed of four parts: the construction of the water network construction effect index system; the determination of the index weight; the construction of the matter-element cloud model evaluation model; and the construction of the prediction model.

Step 1: Based on DPSIR mode, an evaluation system of water network construction effect was built from four dimensions: optimal allocation of water resources; flood control and disaster reduction in river basins; protection of water ecosystems; and intelligent water network management;

Step 2: Through the actual survey data, apply the game combination method to determine the weight of each indicator;

Step 3: Use the matter-element cloud model to calculate the degree of correlation between the water network effect evaluation index and the standard cloud to evaluate the water network construction effect and put forward suggestions for improvement;

Step 4: Use the gray prediction model to predict the effect of future water network construction and compare and analyze it with the planning data.

To sum up, the specific flow of the water network construction effect evaluation based on the game-weighting matter-element cloud model used in this paper is shown in Figure 3 below.

This research model conducted a comprehensive analysis of the effectiveness of water network construction to provide a scientific and accurate evaluation. This model incorporated key steps, such as indicator weight calculation, relevance calculation, and GM(1,1) model prediction, to comprehensively assess the effectiveness of water network construction from temporal and spatial perspectives. Firstly, for indicator weight calculation, the model employed a systematic approach to ensure the accuracy and scientific validity of the weights. Secondly, relevance calculation served as another important step in the model, which was accomplished through methods, such as statistical analysis and relevance coefficient calculation, to reveal the impact of indicators on the effectiveness of water network construction. In the prediction process of the GM(1,1) model, the model utilized the grey system theory, enabling reliable predictions by modeling and analyzing existing data. This allowed the model to forecast future trends in the effectiveness of water network construction and provide an important reference for decision-making.

To achieve a reasonable evaluation and control costs, it is crucial to ensure that data collection focuses only on data closely related to the research objectives, avoiding unnecessary data collection. Furthermore, after data collection, data cleaning and preprocessing should be performed, including data transformation and standardization for subsequent computation and analysis processes. Parallel computing techniques or optimized algorithms can be considered during the processes of indicator weight calculation and correlation calculation to accelerate computation speed and improve efficiency. Effectively utilizing existing data for modeling and analysis is an efficient approach in the prediction process of the GM(1,1) model. This reduces excessive reliance on new data collection, minimizing data costs and time expenses. Additionally, collaboration and data sharing with other research institutions or experts contribute to reducing redundant data collection and processing tasks, thereby improving the efficiency of data utilization. By engaging in cooperative sharing of data resources and research outcomes, data utilization efficiency can be enhanced.

3. Application Examples

3.1. Data Sources and Implementation Cost

The research data mainly come from Xinyang Statistical Yearbook, Xinyang City Water Function Zone Water Resources Quality Report, Xinyang City Water Resources Bulletin, Pingdingshan Statistical Yearbook, Pingdingshan City Water Function Zone Water Resources Quality Report, Pingdingshan City Water Resources Bulletin, Henan Province Statistical Yearbook, Henan Province Water Resources Bulletin, etc. The planning data mainly come from Xinyang City Four Rivers Tongzhi Planning, Xinyang City Water Resources Comprehensive Planning, Pingdingshan. The original data of the evaluation year were collected, collated, and calculated by the Pingdingshan City Water Resources Comprehensive Planning and Pingdingshan City Water Resources Comprehensive Planning.

This study primarily collects the required information from local statistical data, which is part of the public big data and are updated on an annual basis. These data, combined with the actual situation, provide reliable research data as the basis for analysis. The research method supports decision-making in multidimensional and diversified requirements, enabling targeted and comprehensive decision-making. This method applies big data analytics thinking to address sequential decision-making issues in the construction of multifunctional water networks without the need to build a separate platform. It involves step-by-step analysis operations, making it simple and efficient. During the research implementation process, it requires the involvement of professional technicians to perform relevant tasks. This includes verifying the accuracy of basic data, scientifically determining process parameters, and selecting appropriate comparative cases to ensure the reliability and comparability of the study. In summary, implementing this method involves preparatory work, such as acquiring public data and conducting surveys on specific requirements. It also requires hands-on operation by professional personnel to ensure the reliability of the research data source and the feasibility of the method, providing strong support for decision-making.

3.2. Evaluation Level Division

Whether the evaluation of the effect of water network construction is scientific and reasonable is not only closely related to the selection of indicators but is also determined by the classification of evaluation indicators. The scientific selection of grading standards for indicators plays a key role in the evaluation results. At present, there is no uniform standard for evaluating the effect of water network construction in China. This paper combines the construction effects of the main water network projects, such as the South-to-North Water Diversion Middle and Eastern Lines, and refers to the existing national or industry standards and the related literature in similar evaluations. Through expert consultation, the degree of influence of the 26 basic indicators on the water network is divided into 5 grades from I to V, respectively representing excellent, relatively excellent, passing, poor, and poor, for reference in relevant research. At the same time, taking into account the regional differences, we should make corresponding adjustments according to the actual situation during the specific evaluation and selectively determine the standard value suitable for the evaluation of the water network construction effect in the region, as shown in

Table 2. Criteria for Rating the Effectiveness of Water Network Construction.

Standard	Index		I (Excellent)	II (Better)	III (Pass)	IV (Bad)	V (Worse)
Driving force	D1		[95, 100]	[85, 95)	[70, 85)	[50, 70)	<50
	D2		[25, 30]	[20, 25)	[10, 20)	[5, 10)	<5
	D3		[65, 100]	[60, 65)	[55, 60)	[50, 55)	<50
	D4		[95, 100]	[80, 95)	[60, 80)	[40, 60)	<40
	D5		[95, 100]	[85, 95)	[70, 85)	[50, 70)	<50
	D6		[98, 100]	[85, 98)	[75, 85)	[60, 75)	<60
Pressure	P1		[98, 100]	[90, 98)	[80, 90)	[60, 80)	<60
	P2		[95, 100]	[80, 95)	[75, 80)	[50, 75)	<50
	P3		[95, 100]	[90, 95)	[85, 90)	[70, 85)	<70
	P4		[90, 100]	[70, 90)	[50, 70)	[30, 50)	<30
State	S1		[0, 2]	(2, 5]	(5, 10]	(10, 20]	>20
	S2		Up to the standard Within 5%	Within 5% exceeding the standard	Exceeding the standard 5~10%	Exceeding the standard 10~20%	Exceeding the standard > 20%
	S3		[65, 100]	[60, 65)	[55, 60)	[50, 55)	<50
	S4		[80, 100]	[70, 80)	[60, 70)	[50, 60)	<50
	S5		[0, 0.25)	[0.25, 0.5)	[0.5, 1)	[1, 1.2)	>1.2
Influence	I1		[1.7, 2]	[1.5, 1.7)	[1.3, 1.5)	[1, 1.3)	<1
	I2	North	[15, 100]	[10, 15)	[6, 10)	[4, 6)	<4
		South	[30, 100]	[20, 30)	[10, 20)	[6, 10)	<6
	I3		[2, 2.5]	[1.5, 2)	[1, 1.5)	[0.5, 1)	<0.5
	I4		[0, 0.15)	[0.15, 0.25)	[0.25, 0.5)	[0.5, 0.75)	>0.75
	I5		[0, 10)	[10, 15)	[15, 30)	[30, 40)	>40
	I6		[95, 100]	[90, 95)	[85, 90)	[70, 85)	<70
Response	R1		[80, 100]	[70, 80)	[60, 70)	[50, 60)	<50
	R2		[90, 100]	[80, 90)	[70, 80)	[60, 70)	<60
	R3		[90, 100]	[75, 90)	[60, 75)	[40, 60)	<40
	R4		[80, 100]	[70, 80)	[60, 70)	[50, 60)	<50
	R5		[80, 100]	[60, 80)	[30, 60)	[10, 30)	<10

.

3.3. Index Weight Calculation

In this paper, the subjective weight of the indicators is calculated by combining the Delphi method with the AHP; the objective weight of the indicators is calculated by the entropy weight method, and finally, the weight of the indicators of Xinyang and Pingdingshan is calculated by combining the two through the game combination weighting method, as shown in

Table 3. Game-weighting method for determining weights.

Index Layer	Unit	Xinyang			Pingdingshan
		Objective	Subjective	Combination	Objective	Subjective	Combination
D1	%	0.069	0.054	0.066	0.065	0.050	0.061
D2	%	0.016	0.015	0.016	0.031	0.049	0.036
D3	-	0.017	0.025	0.019	0.016	0.012	0.015
D4	%	0.069	0.068	0.069	0.050	0.025	0.042
D5	%	0.069	0.114	0.077	0.065	0.082	0.070
D6	%	0.069	0.098	0.074	0.065	0.053	0.062
P1	%	0.069	0.043	0.064	0.065	0.085	0.071
P2	%	0.019	0.007	0.017	0.015	0.023	0.017
P3	%	0.016	0.012	0.015	0.015	0.016	0.015
P4	%	0.017	0.016	0.017	0.015	0.028	0.019
S1	%	0.035	0.023	0.032	0.048	0.043	0.046
S2	-	0.069	0.046	0.065	0.065	0.061	0.064
S3	%	0.019	0.008	0.017	0.017	0.012	0.016
S4	%	0.019	0.012	0.017	0.024	0.021	0.023
S5	(per 100 km)	0.034	0.025	0.033	0.032	0.029	0.031
I1	-	0.021	0.024	0.021	0.016	0.016	0.016
I2	%	0.014	0.017	0.015	0.021	0.028	0.023
I3	-	0.027	0.030	0.027	0.043	0.059	0.048
I4	%	0.069	0.085	0.072	0.065	0.075	0.068
I5	%	0.037	0.040	0.038	0.049	0.060	0.052
I6	%	0.035	0.051	0.038	0.031	0.050	0.037
R1	%	0.069	0.059	0.067	0.065	0.038	0.057
R2	%	0.021	0.024	0.022	0.019	0.013	0.017
R3	%	0.069	0.078	0.070	0.065	0.048	0.060
R4	%	0.018	0.011	0.017	0.021	0.017	0.020
R5	%	0.017	0.015	0.017	0.016	0.010	0.014

.

3.4. Calculation and Analysis of Evaluation Results

3.4.1. Standard Cloud Computing

According to Formula (5), calculate the standard cloud of each basic evaluation index level, and the results are shown in

Table 4. Standard cloud for evaluating the effectiveness of water network construction.

Index Layer		I (Excellent)	II (Better)	III (Pass)	IV (Bad)	V (Worse)
D1		(97.5, 0.833, 0.008)	(90, 1.667, 0.017)	(77.5, 3.333, 0.033)	(60, 3.333, 0.033)	(25, 8.333, 0.083)
D2		(27.5, 0.833, 0.008)	(22.5, 0.833, 0.008)	(15, 0.833, 0.008)	(7.5, 0.833, 0.008)	(2.5, 0.833, 0.008)
D3		(82.5, 5.833, 0.011)	(62.5, 0.833, 0.008)	(57.5, 1.667, 0.017)	(55, 1.667, 0.017)	(25, 8.333, 0.083)
D4		(97.5, 0.833, 0.008)	(92.5, 0.833, 0.008)	(70, 3.333, 0.033)	(50, 3.333, 0.033)	(20, 6.667, 0.067)
D5		(97.5, 0.833, 0.008)	(90, 1.667, 0.017)	(77.5, 3.333, 0.033)	(60, 3.333, 0.033)	(25, 8.333, 0.083)
D6		(99, 0.333, 0.003)	(91.5, 2.167, 0.022)	(80, 2.500, 0.025)	(67.5, 2.500, 0.025)	(30, 10.000, 0.100)
P1		(99, 0.333, 0.003)	(94, 1.333, 0.013)	(85, 3.333, 0.033)	(70, 3.333, 0.033)	(30, 10.000, 0.100)
P2		(97.5, 0.833, 0.008)	(87.5, 2.500, 0.025)	(77.5, 4.167, 0.042)	(62.5, 4.167, 0.042)	(25, 8.333, 0.083)
P3		(97.5, 0.833, 0.008)	(92.5, 0.833, 0.008)	(87.5, 2.500, 0.025)	(77.5, 2.500, 0.025)	(35, 11.667, 0.117)
P4		(95, 1.667, 0.017)	(80, 3.333, 0.033)	(60, 3.333, 0.033)	(40, 3.333, 0.033)	(15, 5.000, 0.050)
S1		(1, 0.333, 0.003)	(3.5, 90.500, 0.005)	(7.5, 1.667, 0.017)	(15, 1.667, 0.017)	(60, 13.333, 0.133)
S2		(0.5, 0.167, 0.002)	(3.0, 0.667, 0.007)	(7.5, 0.833, 0.008)	(15.0, 1.667, 0.017)	(60.0, 13.333, 0.13)
S3		(82.5, 5.833, 0.058)	(62.5, 0.833, 0.008)	(57.5, 0.833, 0.008)	(52.5, 0.833, 0.008)	(25, 8.333, 0.083)
S4		(90, 3.333, 0.033)	(75, 1.667, 0.017)	(65, 1.667, 0.017)	(55, 1.667, 0.017)	(25, 8.333, 0.083)
S5		(0.25, 0.083, 0.001)	(0.375, 0.042, 0.00)	(0.75, 0.033, 0.000)	(1.1, 0.033, 0.000)	(1.6, 0.133, 0.001)
I1		(1.85, 0.05, 0.001)	(1.6, 0.033, 0.000)	(1.4, 0.050, 0.001)	(1.15, 0.050, 0.001)	(0.5, 0.167, 0.002)
I2	N	(57.5, 14.167, 0.02)	(12.5, 0.833, 0.008)	(8, 0.333, 0.003)	(5, 0.333, 0.003)	(2, 0.667, 0.007)
	S	(65, 11.667, 0.117)	(25, 1.667, 0.017)	(15, 0.667, 0.007)	(8, 0.667, 0.007)	(3, 1.000, 0.010)
I3		(2.25, 0.083, 0.001)	(1.75, 0.083, 0.001)	(1.25, 0.083, 0.001)	(0.75, 0.083, 0.001)	(0.25, 0.083, 0.001)
I4		(0.075, 0.025, 0.00)	(0.2, 0.017, 0.000)	(0.375, 0.042, 0.00)	(0.625, 0.042, 0.00)	(0.375, 0.13, 0.001)
I5		(5, 1.667, 0.017)	(12.5, 0.833, 0.008)	(22.5, 1.667, 0.017)	(35, 1.667, 0.017)	(70, 10.000, 0.100)
I6		(97.5, 0.833, 0.008)	(92.5, 0.833, 0.008)	(87.5, 2.500, 0.025)	(77.5, 2.500, 0.025)	(35, 11.667, 0.117)
R1		(90, 3.333, 0.033)	(75, 1.667, 0.017)	(65, 1.667, 0.017)	(55, 1.667, 0.017)	(25, 8.333, 0.083)
R2		(95, 1.667, 0.017)	(85, 1.667, 0.017)	(75, 1.667, 0.017)	(65, 1.667, 0.017)	(30, 10.000, 0.100)
R3		(95, 1.667, 0.017)	(82.5, 2.500, 0.025)	(67.5, 3.333, 0.033)	(50, 3.333, 0.033)	(20, 6.667, 0.067)
R4		(90, 3.333, 0.033)	(75, 1.667, 0.017)	(65, 1.667, 0.017)	(55, 1.667, 0.017)	(25, 8.333, 0.083)
R5		(90, 3.333, 0.033)	(70, 3.333, 0.033)	(45, 3.333, 0.033)	(20, 3.333, 0.033)	(5, 1.667, 0.017)

.

Using the evaluation index level standard cloud in Table 4 as the domain, using the matter-element cloud evaluation model for the effectiveness of water network construction, MATLAB 9.4 (R2018a) software was used to calculate the correlation degree of water network construction evaluation indicators in Xinyang City and Pingdingshan City from 2016 to 2017, from 2017 to 2018, from 2018 to 2019, from 2019 to 2020, and from 2020 to 2021. The proportion of black and odorous water elimination in urban built-up areas and the measurement rate of agricultural irrigation water were selected as examples, as shown in Figure 4. Black represents real data, indicating that the selected indicators are constantly approaching the excellent.

3.4.2. Temporal Analysis of Correlation Degree

After coupling the degree of correlation with the weight, the evaluation results of the criterion layer index for the effect of water network construction in 2016–2017, 2017–2018, 2018–2019, 2019–2020, and 2020–2021 are calculated, as shown in Figure 5 and Figure 6.

It can be seen from Figure 5 that the driving force indicator is at level I (excellent) and has been in the process of upgrading since 2016. The pass rate of the control degree of water consumption targets per unit of industrial added value is 100%. The effective utilization coefficient of farmland irrigation water has increased from 0.518 in 2016 to 0.536 in 2020, and the improvement effect is more obvious. The water resources development and utilization index has increased from 20.8 in 2016 to 21.5 in 2020, and the effect is not obvious. This phenomenon is mainly due to the fact that Xinyang City uses the Nanwan Reservoir as its water source and has no other backup water source projects. The annual water supply from the Nanwan Reservoir accounts for more than 75%, and the urban water supply system is extremely dependent on the Nanwan Reservoir. With the rapid economic and social development of Xinyang City, the city’s water requirements for production, life, and ecology are increasing day by day, and the contradiction between supply and demand caused by the lack of water source projects has also become acute. This contradiction between supply and demand will become more serious during the dry season, sudden water pollution incidents, or extreme drought in the basin. Such emergencies will cause major water outage accidents, seriously affecting the normal operation of the city and the normal life of residents. Therefore, the requirement to open up new water sources and backup water sources is particularly urgent. After 2020, with the construction of the Chushandian Reservoir Water Supply Project and the construction of the Chushandian Reservoir and Shihe River Connection Project, the urban area will have two large reservoirs as water sources, supplemented by groundwater and reclaimed water, and the water source structure will tend to be stable. The source water network is unimpeded.

The pressure index is in the state of grade I (excellent). Xinyang City has carried out multiple river and lake water ecological protection and restoration projects, including riverbank protection and restoration, eco-slope construction, ecological ditch construction, artificial wetland construction, water source protection forest construction, and conservation forest construction in some sections of rivers, such as Feisha River, Wu Dao River, and Dongshuang River. With the construction of biological habitats, river corridors, water forms, and low-impact development projects, the proportion of ecological embankments has significantly increased, and the proportion of black and odorous water bodies in urban built-up areas has reached 100% in 2020. With the construction of the water network project, the satisfaction rate of ecological flow in important river and lake control sections has also gradually improved.

The state index is in the state of grade I (excellent), and the total water consumption control has been up to the standard. With the implementation of informatization and intelligentization of water conservancy projects, the metering rate of agricultural irrigation and domestic and industrial water has steadily increased. With the gradual implementation of water network construction, the vertical connectivity of rivers has been greatly improved, and the water shortage rate has gradually been alleviated.

The impact indicator has been gradually upgraded from the status of grade III (pass) in 2016 to the status of grade II (better). Xinyang City is an area with relatively abundant annual rainfall in Henan Province, especially the southern mountainous area, which is also the center of heavy rain. Xinyang City has always attached great importance to the construction of water security projects, such as flood control and waterlogging. However, affected by various factors, such as funds, the flood control and drainage standards of some rivers and sections, such as the Shi River, do not match the urban development; the safety and stability of river banks and riverbeds have not been significantly improved, and the construction standards of cross-river buildings are not high. Problems have always existed. This is also the main reason why the average annual loss rate of flood disasters has not decreased significantly. Although the index of water supply safety factor has been improved, the improvement effect is relatively slow. After 2020, Xinyang City will build a resilient flood control and waterlogging engineering system through the construction of medium-sized reservoirs, standard river improvement, sponge city, and pipeline network renovation. It is planned that by 2035, a project that can effectively deal with the level of a 30-year rainstorm can be completed to ensure the normal operation of the city.

The response index is in the state of grade I (excellent), and it is improving year by year. In terms of water network management and intelligence, before 2019, the means of monitoring and forecasting water regimes and industrial conditions in Xinyang City were not advanced enough to facilitate unified command and dispatch by command departments at all levels and relevant departments. Taking the urban water system as an example, the culvert gates and barrage dams in the water system are all controlled manually and lack online monitoring. When the water level of the main stream rises sharply, or when emergencies, such as pollution, occur upstream, the rise and fall of the gate and dam cannot be controlled in time. Therefore, safety management is extremely labor-intensive and has certain uncertainties. Taking water quality monitoring as an example, the routine monitoring section of the environmental protection department is incomplete, the network transmission speed is slow, and the degree of informatization is not high. It is impossible to track, monitor, sense, and collect in real-time during sampling, which affects the representativeness, effectiveness, and reliability of monitoring results. After 2020, with the vigorous advancement of the country’s intelligence and informatization, the construction of Xinyang’s water network will gradually start. The means of water monitoring and perception in Xinyang City have been gradually improved, steadily forming a water management environment based on “Internet +” and using advanced information technology, such as big data, cloud computing, Internet of Things, BIM+GIS, etc. By 2035, Xinyang City will realize the modern management of the city’s water affairs and support and ensure the efficient operation of the water network.

It can be seen from Figure 6 that the driving force index is at the level I (excellent), the water quality compliance rate of drinking water sources, the control degree of water consumption target per 10,000 RMB of GDP, and the pass rate of water consumption target control degree of industrial added value per 10,000 RMB at 100%. Due to the relatively advanced level of agricultural water conservation, the effective use coefficient of farmland irrigation water has increased from 0.59 in 2016 to 0.62 in 2020, and the water resources development and utilization index has increased from 32.8 in 2016 to 33.3 in 2020. Generally speaking, the development and utilization of surface water in Pingdingshan City belongs to the medium development and utilization area, and there is still a certain development potential. At present, the total storage capacity of various reservoir projects in Pingdingshan City is 3.265 billion m³. Due to the lack of connected water transfer engineering facilities between reservoirs and water systems, it is difficult to implement cross-regional and cross-basin dispatching of water resources. The water supply volume of the project is only 200–300 million m³. The South-to-North Water Diversion has insufficient water consumption, accounting for about 40% of the allocated water, and the benefits of water supply have not been fully utilized. By enhancing surface runoff control capabilities, rationally using rain and flood resources, accelerating the construction of river and lake water system connectivity and water diversion projects, and deploying reservoir water to densely populated areas, the available surface water will be further increased.

The pressure index has always been in the state of V (worse), but it has decreased significantly with the development of water network construction. Before 2016, affected by human activities, local water areas, such as river floodplains, became occupied, urban inland rivers became narrower and narrower, cities hardened, and waterfront vegetation and wetlands were destroyed. At the same time, because most of the rainwater is transported through the pipe network, the catchment conditions of the river have changed, causing the river to lose its source of replenishment and the ecological environment to have insufficient water. This has also resulted in the reduction in water conservation capacity, natural stagnation adjustment capacity, and self-purification capacity of natural rivers in the region and the degradation of ecological functions [65]. At the same time, due to the influence of previous construction concepts, the rainwater and sewage of the pipe network in some areas merged, and the amount of water entering the sewage pipes during heavy rains exceeded its design flow rate, causing rainwater to overflow from the wells laid on the bottom of the inland river and enter the water body to cause slight pollution, black and smelly phenomenon.

The state index is in the state of grade I (excellent), and the total water consumption control meets the requirements. With the implementation of informatization and intelligentization of water conservancy projects, the metering rate of agricultural irrigation and domestic and industrial water has steadily increased. With the gradual implementation of water network construction and the full utilization of the South-to-North Water Diversion, the water shortage rate index has been decreasing year by year.

The impact index is in the state of grade II (better). Pingdingshan City has built a large number of reservoir projects and has carried out comprehensive treatment of important rivers, such as the Beiru River, Shahe River, and Li River, which are the main flood control channels in the territory. At the same time, various degrees of planning and governance have been carried out for the urban and non-urban sections of many small and medium-sized rivers, such as the Zhan River, Shi River, and Hui River. Based on governance, a flood control engineering system based on reservoirs and river embankments has been initially built, which has played a huge role in previous floods. The safety and stability of river banks and riverbeds have increased significantly, but small and medium-sized rivers lack systematic governance, and many water-blocking facilities and illegal buildings occupy the water space, hindering flood discharge and causing problems with flood outlets. At the same time, after the completion of the South-to-North Water Diversion Project, the confluence conditions of some rivers have changed. In addition, the topography and landforms along the line have changed to a certain extent compared with the construction of the main canal, resulting in the difficulty of flood discharge in some channels, and there are certain flood control hazards. Based on the above reasons, the flood control pressure in Pingdingshan City has increased, and the rate of compliance with waterlogging prevention and control has not improved significantly.

The response index is in the state of grade I (excellent). After years of development, modern water management facilities in Pingdingshan City have gradually improved. The current monitoring and perception system includes monitoring types, such as water and rain, groundwater level, gate control, and water quality, which meet business needs to a certain extent. However, the overall management level of Pingdingshan is not high in automation. At the same time, the data exchange mechanism has not been fully established, and resources cannot be fully shared. Since the construction of the water network, with the construction of water conservancy information collection, project monitoring, and network communication, Pingdingshan City has realized the dynamic monitoring and comprehensive perception of water conservancy projects, water resources, water environment, and other information. At the same time, in the process of water network construction, a water conservancy information network government affairs extranet covering the township level and above water conservancy departments of the city has been formed, ensuring the transmission and exchange of various video, voice, and monitoring data required for water conservancy business applications. Through the integration of the water conservancy information network and mobile Internet, the application of video conferencing systems, satellite communication systems, and emergency communication systems in the water conservancy business is realized. Based on the above measures, the intelligentization rate of major water conservancy projects in Pingdingshan City has been effectively improved, ensuring water safety.

To compare the development trend of water network construction effect evaluation more clearly, the comprehensive management degree of the criterion layer in different regions is normalized and represented by a 3D histogram, as shown in Figure 7.

3.4.3. Spatial Analysis of Correlation Degree

It can be seen from Figure 7 that since 2016, Pingdingshan in Xinyang City has maintained a level I (excellent) status in terms of driving force, status, and response indicators, and all impact indicators have maintained a level II (better) status, indicating that since the construction of the water network, water consumption, the degree of control of water consumption targets, the water quality compliance rate of water function zones and drinking water sources, and the compliance of total water consumption control are all getting better and better. With the promotion and application of intelligent information technology in water conservancy projects, the intelligent control rate of major water conservancy projects, the coverage rate of important river and lake monitoring stations, and the metering rate of agricultural irrigation and domestic and industrial water have all increased significantly. The main difference between the two cities lies in the pressure indicators, among which the pressure indicators of Xinyang City are in the state of level I (excellent), while the pressure indicators of Pingdingshan City are in the state of level V (worse). The reason is that the standard rate of grade 1–5 dikes and the ratio of ecological dikes in Pingdingshan City are low; the urban hardening engineering technology is not advanced; most of the rainwater is transported by the pipe network, and the infiltration and slope drainage in the water cycle have completely become concentrated in the river, and the flood control of the river is mainly based on masonry hardening, which destroys the vegetation and wetlands along the river, which leads to changes in the water and soil exchange conditions of the river, and the ecological functions of the river, such as water conservation, natural stagnation regulation, and self-purification degradation; seasonal cut-offs increased significantly. After 2016, especially after 2020, the construction of the water network will be gradually accelerated. Based on the existing rivers, lakes, reservoirs, and canals, Pingdingshan City will construct projects, such as rain and sewage diversion, ecological transformation of rivers and canals, water system connection, water diversion, and wetlands; the natural wetland of the Huihe Estuary gradual plays a regulating role, and the bank slope vegetation planted in a scientific proportion gradually forms a good ecological effect. In the future, Pingdingshan City will use large-scale reservoirs as the core of regulation and natural river ditches and connection projects as channels to continue to implement ecological transformation. Pollution control and other engineering and non-engineering measures will gradually recover the ecological health of rivers and lakes, and the pressure indicators will rise.

3.5. Comparative Analysis of Prediction and Planning Data

The prediction of the effect of planning implementation is part of the evaluation of the effect of water network construction. It is helpful to understand the changing trend of the construction effect level of the water network construction area in the future and then take corresponding improvement measures in a targeted manner to ensure the effective implementation of planning data. This paper uses the GM(1,1) model to fit the data of Xinyang City and Pingdingshan City from 2016 to 2021 according to the Formulas (11)–(17) and compares them with the planning data of the local water network construction in 2035 and selects the urban built-up area to eliminate black and odorous water bodies. The proportion, the metering rate of agricultural irrigation water, the development and utilization of water resources, and the water shortage rate indicators are used as examples to make a fitting graph, as shown in Figure 8.

The predicted value shows that the water network construction effect of Xinyang City and Pingdingshan City has maintained a good trend and can basically meet the planning values of various indicators by the planning level year, and the DPSIR index gradually tends to the I (excellent) state. Among them, the predicted value of water resource utilization in Pingdingshan City in 2035 is 32.33%, which is less than 38% of the planning expectation, and the utilization of unconventional water resources is insufficient. The situation of unreasonable water use structure and weak water resources dispatching ability still needs to be improved in the future. The planned value of the water shortage rate in Xinyang City in 2035 is 0, and the predicted value is 1.99%. The reason is that although Xinyang City has formed a joint water supply situation of Chushandian Reservoir and Nanwan Reservoir, with the economic growth of Xinyang City year by year, the water source project contradiction between supply and demand caused by the shortage will still exist for a long time. In order to solve such problems, we should accelerate the construction of water network projects, build a network of urban water circulation systems, and implement many comprehensive improvement projects for rivers, lakes, and reservoirs in combination with the management of small and medium-sized rivers. The establishment of an advanced and practical intelligent application platform has realized the comprehensive integration and sharing of public water data, promoted the refined management of regional water comprehensive business, and improved the level of scientific decision-making and scheduling management.

4. Conclusions

This paper studies the effect evaluation of water network construction. First, based on analyzing the relevant connotations of water network construction, the water network is constructed with the criteria of driving force (D), pressure (P), state (S), impact (I), and response (R) as the starting points. After an effective evaluation system is built, the indicator weight is determined through the game combination weighting, and the water network construction effect evaluation model based on the matter-element cloud model is established, which can well overcome the ambiguity and uncertainty of the data. This paper draws the following conclusions: (1) The effect of water network construction in the study area is constantly improving, but the impact index of Xinyang is in the state of II (better), the pressure index of Pingdingshan is in the state of V (worse), and the impact index is in the state of II (better) status, so there is still a lot of room for development in water network construction; (2) The results of the regional analysis and prediction in the study area are consistent with the actual development situation of the region. Targeted measures and suggestions are proposed for the two regions from the perspectives of efficient utilization of water resources, restoration of water ecosystems, comprehensive management of water environment, scientific prevention and control of water disasters, and intelligent management of water projects, indicating the effectiveness of the indicator system and evaluation method established in this paper.

The main contributions of this study are as follows: (1) The evaluation index system of water network construction effect based on the DPSIR model was established. It effectively integrates the four dimensions of optimal allocation of water resources, flood control and disaster reduction in river basins, protection of water ecosystems, and wisdom of water network management, making the evaluation indicators closer to reality. This index system can provide a reference for sustainable development evaluation standards in other related fields of water conservancy; (2) In order to objectively evaluate the effect of water network construction, this study introduces the game combination weighting method and the evaluation of the matter-element cloud model. The evaluation model can overcome the subjectivity of experts and the ambiguity and uncertainty of index data in the evaluation process. The case evaluation shows that this method has strong practicability and can be used for evaluation in other industries; (3) This study enriches the development theory of water network construction and is conducive to promoting the sustainable development of water conservancy infrastructure. At the same time, it is of practical value to choose the appropriate evaluation criteria for the government to achieve the goal of sustainable development of water conservancy.