Water

Research

30 pages, 8396 KiB

Open AccessArticle

Community Agricultural Reservoir Construction and Water Supply Network Design in Ubon Ratchathani, Thailand, Using Adjusted Variable Neighborhood Strategy Adaptive Search

by Rerkchai Srivoramasa, Natthapong Nanthasamroeng, Rapeepan Pitakaso, Thanatkij Srichok, Surajet Khonjun, Worapot Sirirak and Chalermchat Theeraviriya

Water 2023, 15(3), 591; https://doi.org/10.3390/w15030591 - 2 Feb 2023

Viewed by 3463

Abstract

Agricultural sectors all over the world are facing water deficiencies as a result of various factors. Countries in the Greater Mekong Subregion (GMS) in particular depend on the production of agricultural products; thus, drought has become a critical problem in such countries. The [...] Read more.

Agricultural sectors all over the world are facing water deficiencies as a result of various factors. Countries in the Greater Mekong Subregion (GMS) in particular depend on the production of agricultural products; thus, drought has become a critical problem in such countries. The average water level in the lower part of the Mekong River has been decreasing dramatically, resulting in the wider agricultural area of the Mekong watershed facing a lack of water for production. The construction of community reservoirs and associated water supply networks represents a strategy that can be used to address drought problems in the GMS. This study aims to solve the agricultural community reservoir establishment and water supply network design (CR–WSND) problem in Khong Chiam, Ubon Ratchathani, Thailand—a city located in the Mekong Basin. The CR–WSND model is formulated using mixed-integer programming (MIP) in order to minimize the cost of reservoir construction and water irrigation. An adjusted variable neighborhood strategy adaptive search (A-VaNSAS) is applied to a real-world scenario involving 218 nodes, and its performance is compared with that of the original variable neighborhood strategy adaptive search (VaNSAS), differential evolution (DE), and genetic algorithm (GA) approaches. An improved box selection formula and newly designed improvement black boxes are added to enhance the quality beyond the original VaNSAS. The results reveal that the quality of the solution from A-VaNSAS is significantly better than those of GA, DE, and VaNSAS (by 6.27%, 9.70%, and 9.65%, respectively); thus, A-VaNSAS can be used to design a community reservoir and water supply network effectively. Full article

(This article belongs to the Special Issue Using Artificial Intelligence for Smart Water Management)

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21 pages, 7130 KiB

Open AccessArticle

Study on Forecasting Break-Up Date of River Ice in Heilongjiang Province Based on LSTM and CEEMDAN

by Mingyang Liu, Yinan Wang, Zhenxiang Xing, Xinlei Wang and Qiang Fu

Water 2023, 15(3), 496; https://doi.org/10.3390/w15030496 - 26 Jan 2023

Cited by 2 | Viewed by 1958

Abstract

In spring, rivers at middle and high latitudes in the Northern Hemisphere are prone to ice jams, which threaten the safety of hydraulic structures in rivers. Heilongjiang Province is located on the highest latitude in China, starting at 43°26′ N and reaching 53°33′ [...] Read more.

In spring, rivers at middle and high latitudes in the Northern Hemisphere are prone to ice jams, which threaten the safety of hydraulic structures in rivers. Heilongjiang Province is located on the highest latitude in China, starting at 43°26′ N and reaching 53°33′ N. Rivers in Heilongjiang Province freeze in winter and break up in spring. Forecasting the break-up date of river ice accurately can provide an important reference for the command, dispatch, and decision-making of ice flood preventing and shipping. Based on the observed break-up date series of river ice from seven representative hydrological stations in Heilongjiang Province, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was used to decompose the observed break-up date series of river ice into several subsequences, and the long-short term memory neural network (LSTM) was used to forecast the subsequences decomposed by CEEDMAN. Then, the forecast results of each subsequence were summed to obtain the forecasting value for the break-up date of river ice proceeded by CEEMDAN-LSTM. Compared with the LSTM, the forecast accuracy of CEEMDAN-LSTM for the break-up date of river ice had been significantly improved, with the mean absolute error reduced from 0.80–6.40 to 0.75–3.40, the qualification rate increased from 60–100% to 80–100%, the root-mean-square difference reduced from 1.37–5.97 to 0.95–1.69, the correlation coefficient increased from 0.51–0.97 to 0.97–0.98, and the Taylor skill score increased from 0.87–0.99 to 0.99. CEEMDAN-LSTM performed well in forecasting the break-up date of river ice in the Heilongjiang Province, which can provide important information for command, dispatch, and decision-making of ice flood control. Full article

(This article belongs to the Special Issue Using Artificial Intelligence for Smart Water Management)

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14 pages, 3194 KiB

Open AccessArticle

Comparison of Two Convergence Criterion in the Optimization Process Using a Recursive Method in a Multi-Reservoir System

by Rosalva Mendoza-Ramírez, Rodolfo Silva, Ramón Domínguez-Mora, Eduardo Juan-Diego and Eliseo Carrizosa-Elizondo

Water 2022, 14(19), 2952; https://doi.org/10.3390/w14192952 - 21 Sep 2022

Viewed by 1785

Abstract

Stochastic dynamic programming (SDP) is an optimization technique used in the operation of reservoirs for many years. However, being an iterative method requiring considerable computational time, it is important to establish adequate convergence criterion for its most effective use. Based on two previous [...] Read more.

Stochastic dynamic programming (SDP) is an optimization technique used in the operation of reservoirs for many years. However, being an iterative method requiring considerable computational time, it is important to establish adequate convergence criterion for its most effective use. Based on two previous studies for the optimization of operations in one of the most important multi-reservoir systems in Mexico, this work uses SDP, centred on the interest in the convergence criterion used in the optimization process. In the first trial, following the recommendations in the literature consulted, the difference in the absolute value of two consecutive iterations was taken and compared against a set tolerance value and a discount factor. In the second trial, it was decided to take the squared difference of the two consecutive iterations. In each of the trials, the computational time taken to obtain the optimal operating policy was quantified, along with whether the optimal operating policy was obtained by meeting the convergence criterion or by reaching the maximum number of iterations. With each optimization policy, the operation of the system under study was simulated and four variables were taken as evaluators of the system behaviour. The results showed few differences in the two operation policies but notable differences in the computation time used in the optimization process, as well as in the fulfilment of the convergence criterion. Full article

(This article belongs to the Special Issue Using Artificial Intelligence for Smart Water Management)

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22 pages, 6149 KiB

Open AccessArticle

Development of a Revised Multi-Layer Perceptron Model for Dam Inflow Prediction

by Hyeon Seok Choi, Joong Hoon Kim, Eui Hoon Lee and Sun-Kwon Yoon

Water 2022, 14(12), 1878; https://doi.org/10.3390/w14121878 - 10 Jun 2022

Cited by 7 | Viewed by 2320

Abstract

It is necessary to predict dam inflow in advance for flood prevention and stable dam operations. Although predictive models using deep learning are increasingly studied, these existing studies have merely applied the models or adapted the model structure. In this study, data preprocessing [...] Read more.

It is necessary to predict dam inflow in advance for flood prevention and stable dam operations. Although predictive models using deep learning are increasingly studied, these existing studies have merely applied the models or adapted the model structure. In this study, data preprocessing and machine learning algorithms were improved to increase the accuracy of the predictive model. Data preprocessing was divided into two types: The learning method, which distinguishes between peak and off seasons, and the data normalization method. To search for a global solution, the model algorithm was improved by adding a random search algorithm to the gradient descent of the Multi-Layer Perceptron (MLP) method. This revised model was applied to the Soyang Dam Basin in South Korea, and deep learning-based discharge prediction was performed using historical data from 2004 to 2021. Data preprocessing improved the accuracy by up to 61.5%, and the revised model improved the accuracy by up to 40.3%. With the improved algorithm, the accuracy of dam inflow predictions increased to 89.4%. Based on these results, stable dam operation is possible through more accurate inflow predictions. Full article

(This article belongs to the Special Issue Using Artificial Intelligence for Smart Water Management)

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16 pages, 757 KiB

Open AccessArticle

An Improved Transfer Learning Model for Cyanobacterial Bloom Concentration Prediction

by Jianjun Ni, Ruping Liu, Yingqi Li, Guangyi Tang and Pengfei Shi

Water 2022, 14(8), 1300; https://doi.org/10.3390/w14081300 - 16 Apr 2022

Cited by 8 | Viewed by 2328

Abstract

The outbreak of cyanobacterial blooms is a serious water environmental problem, and the harm it brings to aquatic ecosystems and water supply systems cannot be underestimated. It is very important to establish an accurate prediction model of cyanobacterial bloom concentration, which is a [...] Read more.

The outbreak of cyanobacterial blooms is a serious water environmental problem, and the harm it brings to aquatic ecosystems and water supply systems cannot be underestimated. It is very important to establish an accurate prediction model of cyanobacterial bloom concentration, which is a challenging issue. Machine learning techniques can improve the prediction accuracy, but a large amount of historical monitoring data is needed to train these models. For some waters with an inconvenient geographical location or frequent sensor failures, there are not enough historical data to train the model. To deal with this problem, a fused model based on a transfer learning method is proposed in this paper. In this study, the data of water environment with a large amount of historical monitoring data are taken as the source domain in order to learn the knowledge of cyanobacterial bloom growth characteristics and train the prediction model. The data of the water environment with a small amount of historical monitoring data are taken as the target domain in order to load the model trained in the source domain. Then, the training set of the target domain is used to participate in the inter-layer fine-tuning training of the model to obtain the transfer learning model. At last, the transfer learning model is fused with a convolutional neural network to obtain the prediction model. Various experiments are conducted for a 2 h prediction on the test set of the target domain. The results show that the proposed model can significantly improve the prediction accuracy of cyanobacterial blooms for the water environment with a low data volume. Full article

(This article belongs to the Special Issue Using Artificial Intelligence for Smart Water Management)

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16 pages, 14949 KiB

Open AccessFeature PaperArticle

Flood Season Staging and Adjustment of Limited Water Level for a Multi-Purpose Reservoir

by Chongxun Mo, Juan Deng, Xingbi Lei, Yuli Ruan, Shufeng Lai, Guikai Sun and Zhenxiang Xing

Water 2022, 14(5), 775; https://doi.org/10.3390/w14050775 - 28 Feb 2022

Cited by 11 | Viewed by 2556

Abstract

A reasonable flood season delineation can effectively implement staged reservoir scheduling and improve water resource efficiency. Therefore, this study is aimed at analyzing the flood period segmentation and optimizing the staged flood limit water levels (FLWLs) for a multi-purpose reservoir, the Longtan Reservoir, [...] Read more.

A reasonable flood season delineation can effectively implement staged reservoir scheduling and improve water resource efficiency. Therefore, this study is aimed at analyzing the flood period segmentation and optimizing the staged flood limit water levels (FLWLs) for a multi-purpose reservoir, the Longtan Reservoir, China. The rainfall seasonality index (

S I_{P}

) and the runoff seasonality index (

S I_{R}

) are used to evaluate the feasibility and rationality of the flood period staging. The fractal method is then used to segment the flood season. Finally, the design flood is carried out to optimize the staged FLWLs. The results show that the

S I

is an effective indicator for judging the feasibility and verifying the rationality of flood segmentation. The flood period can be segmented into the pre-flood season (12 April–29 May), the main flood season (30 May–3 September), and the post-flood season (4 September–9 November). The FLWLs in the main flood and the post-flood season can be raised by 2.05 m and 3.45 m, and the effective reservoir capacity is increased by 5.810 billion m³ and 6.337 billion m³, according to the results of the flood season division. Full article

(This article belongs to the Special Issue Using Artificial Intelligence for Smart Water Management)

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27 pages, 8534 KiB

Open AccessArticle

Study on the Optimal Operation of a Hydropower Plant Group Based on the Stochastic Dynamic Programming with Consideration for Runoff Uncertainty

by Hongxue Zhang, Lianpeng Zhang, Jianxia Chang, Yunyun Li, Ruihao Long and Zhenxiang Xing

Water 2022, 14(2), 220; https://doi.org/10.3390/w14020220 - 12 Jan 2022

Cited by 2 | Viewed by 1899

Abstract

Hydropower plant operation reorganizes the temporal and spatial distribution of water resources to promote the comprehensive utilization of water resources in the basin. However, a lot of uncertainties were brought to light concerning cascade hydropower plant operation with the introduction of the stochastic [...] Read more.

Hydropower plant operation reorganizes the temporal and spatial distribution of water resources to promote the comprehensive utilization of water resources in the basin. However, a lot of uncertainties were brought to light concerning cascade hydropower plant operation with the introduction of the stochastic process of incoming runoff. Therefore, it is of guiding significance for the practice operation to investigate the stochastic operation of cascade hydropower plants while considering runoff uncertainty. The runoff simulation model was constructed by taking the cascade hydropower plants in the lower reaches of the Lancang River as the research object, and combining their data with the copula joint function and Gibbs method, and a Markov chain was adopted to construct the transfer matrix of runoff between adjacent months. With consideration for the uncertainty of inflow runoff, the stochastic optimal operation model of cascade hydropower plants was constructed and solved by the SDP algorithm. The results showed that 71.12% of the simulated monthly inflow of 5000 groups in the Nuozhadu hydropower plant drop into the reasonable range. Due to the insufficiency of measured runoff, there were too many 0 values in the derived transfer probability, but after the simulated runoff series were introduced, the results significantly improved. Taking the transfer probability matrix of simulated runoff as the input of the stochastic optimal operation model of the cascade hydropower plants, the operation diagram containing the future-period incoming water information was obtained, which could directly provide a reference for the optimal operation of the Nuozhadu hydropower plant. In addition, taking the incoming runoff process in a normal year as the standard, the annual mean power generation based on stochastic dynamic programming was similar to that based on dynamic programming (respectively 305.97 × 10⁸

k W \cdot h

and 306.91 × 10⁸

k W \cdot h

), which proved that the operation diagram constructed in this study was reasonable. Full article

(This article belongs to the Special Issue Using Artificial Intelligence for Smart Water Management)

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12 pages, 6621 KiB

Open AccessArticle

Underwater Biological Detection Algorithm Based on Improved Faster-RCNN

by Pengfei Shi, Xiwang Xu, Jianjun Ni, Yuanxue Xin, Weisheng Huang and Song Han

Water 2021, 13(17), 2420; https://doi.org/10.3390/w13172420 - 3 Sep 2021

Cited by 24 | Viewed by 3422

Abstract

Underwater organisms are an important part of the underwater ecological environment. More and more attention has been paid to the perception of underwater ecological environment by intelligent means, such as machine vision. However, many objective reasons affect the accuracy of underwater biological detection, [...] Read more.

Underwater organisms are an important part of the underwater ecological environment. More and more attention has been paid to the perception of underwater ecological environment by intelligent means, such as machine vision. However, many objective reasons affect the accuracy of underwater biological detection, such as the low-quality image, different sizes or shapes, and overlapping or occlusion of underwater organisms. Therefore, this paper proposes an underwater biological detection algorithm based on improved Faster-RCNN. Firstly, the ResNet is used as the backbone feature extraction network of Faster-RCNN. Then, BiFPN (Bidirectional Feature Pyramid Network) is used to build a ResNet–BiFPN structure which can improve the capability of feature extraction and multi-scale feature fusion. Additionally, EIoU (Effective IoU) is used to replace IoU to reduce the proportion of redundant bounding boxes in the training data. Moreover, K-means++ clustering is used to generate more suitable anchor boxes to improve detection accuracy. Finally, the experimental results show that the detection accuracy of underwater biological detection algorithm based on improved Faster-RCNN on URPC2018 dataset is improved to 88.94%, which is 8.26% higher than Faster-RCNN. The results fully prove the effectiveness of the proposed algorithm. Full article

(This article belongs to the Special Issue Using Artificial Intelligence for Smart Water Management)

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