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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Water Management".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 21030

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Special Issue Editors

Prof. Dr. Ozgur Kisi

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Department of Civil Engineering, Faculty of Natural Sciences and Engineering Ilia State University, 0162 Tbilisi, Georgia
Interests: developing novel algorithms and methods towards the innovative solution of hydrologic forecasting and modeling; suspended sediment modeling; forecasting; estimating; spatial and temporal analysis of hydro-climatic variables such as precipitation; streamflow; suspended sediment; evaporation; evapotranspiration; groundwater; lake level and water quality parameters; hydro-informatics
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Dr. Rana Muhammad Adnan

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State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
Interests: sustainable development; water resources management; hydrological modeling; artificial intelligence; time series analysis; rainfall–runoff relationship; wind energy; sediment load; evaporation; evapotranspiration; hydro-meteorological droughts; groundwater; water quality parameters modeling; novel meta-heuristic approaches applications; trend analysis; clustering; watershed planning and management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

For the accurate management of water resources, prediction and/or estimation of the nonlinear phenomena (e.g., the parameters related to hydrological cycle) are mostly required. With the effect of climate change and population growth in most parts of the world, finding a solution for such problems is much more challenging, and this problem can be addressed through the use of advanced computational tools. The rational management of a city and its infrastructure in response to increased pollution, climate change, and natural and other disasters, for daily operation and emergency response, is becoming critical to enhance livability for citizens. Creating healthy, sustainable urban environments necessitates advanced numerical tools for optimal design and management processes. Extreme weather events cause numerous economic and life losses in the changing climate and environment. It is, therefore, important to keep developing and improving our knowledge in the field of extreme weather vulnerability assessment and hazard alleviation. The main aim of this Special Issue is to explore various implementations of machine learning methods (MLM) improved with metaheuristic algorithms (MAs) to advance prediction and/or modeling hydrological/water resources phenomena which have vital importance in the management of water resources. The topics of this Special Issue include but are not limited to:

Forecasting sustainable water resources variables (modeling streamflow, sediment, groundwater, lake level, evaporation, evapotranspiration etc.) with advanced MLM;
Optimization of available water resources with advanced computing methods;
Probabilistic and susceptibility studies with artificial intelligence to sustain water resources;
Spatial and temporal extreme events modeling with novel models to conserve water resources;
Implementation of MLM with new metaheuristic algorithms in water resources;
Reservoir operation using Mas;
Ensemble modeling procedure with MLM in water resources;
Application of conjunction MLM such as wavelet or EEMD-based MLM.

Prof. Dr. Ozgur Kisi
Dr. Rana Muhammad Adnan
Guest Editors

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Keywords

sustainability in water resources management
machine learning in WR
hybrid modeling with MLM
hydrologic modeling with advanced MLM
mas implementation in WR

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Published Papers (6 papers)

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Research

22 pages, 1522 KiB

Open AccessArticle

Identification and Prioritization of Tourism Development Strategies Using SWOT, QSPM, and AHP: A Case Study of Changbai Mountain in China

by Ping Fan, Yihao Zhu, Zi Ye, Guodao Zhang, Shanchuan Gu, Qi Shen, Sarita Gajbhiye Meshram and Ehsan Alvandi

Sustainability 2023, 15(6), 4962; https://doi.org/10.3390/su15064962 - 10 Mar 2023

Cited by 15 | Viewed by 4023

Abstract

This research was conducted with the objective of identifying and ranking the tourism sector development strategies for the Changbai Mountain. The SWOT approach was used to construct strategies and the QSPM matrix and AHP method were employed to rank selected strategies. A questionnaire and the Delphi technique were used to collect and analyze research data from forty specialists. The effectiveness of 16 internal factors and 12 external factors in the business region was assessed. According to the results obtained, “Existence of beautiful natural features with distinctive scenery” is the most significant strength of Changbai Mountain. Also, “Inadequate amenities and weakness of infrastructure construction” has been established as the most significant weakness. The term “Adapting the development of the region to the national strategy” is among the most significant external opportunities. Additionally, the “islanding phenomenon” is one of the most significant threats. Sixteen plans were recommended for the growth of the Changbai Mountain’s tourism business. On the basis of the matrix of internal–external components in the SWOT model, an offensive strategy was identified as the optimal approach. We used the combined SWOT-AHP model with 4 criteria and 28 sub-criteria to determine the optimum strategy in the second model, and offensive methods were given the highest priority. The results showed that the “Taking advantage of the natural, historical potentials, etc.” and “Establishing an appropriate mechanism for public and private sector investment” strategies are the most crucial for improving the condition in Changbai Mountain. Therefore, special consideration should be given to the tourism potential in this region, and it should be placed on the agenda of managers and planners in order to strengthen the tourism industry, the region’s economic status, and create employment opportunities. Full article

(This article belongs to the Special Issue Sustainable Management of Water and Environment with the Aid of Advanced Computing Methods)

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21 pages, 1439 KiB

Open AccessArticle

Investigation of West Lake Ecotourism Capabilities Using SWOT and TOPSIS Decision-Making Methods

by Yihao Zhu, Chou Chen, Guodao Zhang, Zimin Lin, Sarita Gajbhiye Meshram and Ehsan Alvandi

Sustainability 2023, 15(3), 2464; https://doi.org/10.3390/su15032464 - 30 Jan 2023

Cited by 13 | Viewed by 2938

Abstract

Using SWOT and TOPSIS models, this study aimed to determine West Lake’s potential as a tourist destination. In terms of study methodology, the current research is a descriptive survey. The TOPSIS method was used to rank strengths, weaknesses, threats, opportunities, and preferred strategies after the SWOT analysis was completed. Using a questionnaire and the Delphi method, 30 regional specialists provided research data which was collected and analyzed. Thirteen internal elements and twelve external factors affecting the West Lake tourism were identified and evaluated. Additionally, fifteen strategies were presented to improve the lake’s tourism. On the basis of the matrix of internal–external components in the SWOT model, an aggressive approach was determined to be the optimal strategic stance for West Lake. The results of the TOPSIS technique also revealed that internal strengths have a bigger impact than other elements; hence, aggressive strategies are emphasized for the growth of ecotourism in this region. Based on the results of the TOPSIS technique, the “optimal utilization of the lake’s natural, cultural, and historical potential and attractions in order to develop and attract tourists and generate jobs and revenues“ and “formulation of strategic plans to maximize potential and opportunities in order to attract tourists in all seasons of the year“ strategies were identified as the most important strategies for enhancing the West Lake tourism scenario. Therefore, it is hoped that the relevant authorities would contribute to the expansion and enhancement of the region’s economy through a focus on the vision and goals of the tourist sector and careful foresight in the implementation of these projects. Full article

(This article belongs to the Special Issue Sustainable Management of Water and Environment with the Aid of Advanced Computing Methods)

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14 pages, 6589 KiB

Open AccessArticle

Extracting Optimal Operation Rule Curves of Multi-Reservoir System Using Atom Search Optimization, Genetic Programming and Wind Driven Optimization

by Suwapat Kosasaeng, Nirat Yamoat, Seyed Mohammad Ashrafi and Anongrit Kangrang

Sustainability 2022, 14(23), 16205; https://doi.org/10.3390/su142316205 - 5 Dec 2022

Cited by 6 | Viewed by 2261

Abstract

This research aims to apply optimization techniques using atom search optimization (ASO), genetic programming (GP), and wind-driven optimization (WDO) with a reservoir simulation model for searching optimal rule curves of a multi-reservoir system, using the objective function with the minimum average quantity of release excess water. The multi-reservoir system consisted of five reservoirs managed by a single reservoir that caused severe problems in Sakon Nakhon province, Thailand, which was hit by floods in 2017. These included Huai Nam Bo Reservoir, the Upper Huai Sai-1 Reservoir, the Upper Huai Sai-2 Reservoir, the Upper Huai Sai-3 Reservoir, and the Huai Sai Khamin Reservoir. In this study, the monthly reservoir rule curves, the average monthly inflow to the reservoirs during 2005–2020, the water demand of the reservoirs, hydrological data, and physical data of the reservoirs were considered. In addition, the performance of the newly obtained rule curves was evaluated by comparing the operation with a single reservoir and the operation with a multi-reservoir network. The results showed situations of water shortage and water in terms of frequency, duration, average water, and maximum water. The newly obtained rule curves from the multi-reservoir system case showed an average water excess of 43.722 MCM/year, which was less than the optimal curves from the single reservoir case, where the average water excess was 45.562 MCM/year. An analysis of the downstream reservoir of the multi-reservoir system, which diverts water from the upstream reservoirs, was performed. The results showed that the new optimal rule curves of ASO, GP, and WDO operated as a multi-reservoir system performed better than when operated as a single reservoir. Therefore, this research is suitable for sustainable water management without construction. Full article

(This article belongs to the Special Issue Sustainable Management of Water and Environment with the Aid of Advanced Computing Methods)

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19 pages, 8540 KiB

Open AccessArticle

Evaluation of Machine Learning versus Empirical Models for Monthly Reference Evapotranspiration Estimation in Uttar Pradesh and Uttarakhand States, India

by Priya Rai, Pravendra Kumar, Nadhir Al-Ansari and Anurag Malik

Sustainability 2022, 14(10), 5771; https://doi.org/10.3390/su14105771 - 10 May 2022

Cited by 14 | Viewed by 2191

Abstract

Reference evapotranspiration (ET_o) plays an important role in agriculture applications such as irrigation scheduling, crop simulation, water budgeting, and reservoir operations. Therefore, the accurate estimation of ET_o is essential for optimal utilization of available water resources on regional and global scales. The present study was conducted to estimate the monthly ET_o at Nagina (Uttar Pradesh State) and Pantnagar (Uttarakhand State) stations by employing the three ML (machine learning) techniques including the SVM (support vector machine), M5P (M5P model tree), and RF (random forest) against the three empirical models (i.e., Valiantzas-1: V-1, Valiantzas-2: V-2, Valiantzas-3: V-3). Three different input combinations (i.e., C-1, C-2, C-3) were formulated by using 8-year (2009–2016) climatic data of wind speed (u), solar radiation (R_s), relative humidity (RH), and mean air temperature (T) recorded at both stations. The predictive efficacy of ML and the empirical models was evaluated based on five statistical indicators i.e., CC (correlation coefficient), WI (Willmott index), EC (efficiency coefficient), RMSE (root mean square error), and MAE (mean absolute error) presented through a heatmap along with graphical interpretation (Taylor diagram, time-series, and scatter plots). The results showed that the SVM-1 model corresponding to the C-1 input combination outperformed the other ML and empirical models at both stations. Moreover, the SVM-1 model had the lowest MAE (0.076, 0.047 mm/month) and RMSE (0.110, 0.063 mm/month), and highest EC (0.995, 0.999), CC (0.998, 0.999), and WI (0.999, 1.000) values during validation period at Nagina and Pantnagar stations, respectively, and closely followed by the M5P model. Consequently, the ML model (i.e., SVM) was found to be more robust, and reliable in monthly ET_o estimation and can be used as a promising alternative to empirical models at both study locations. Full article

(This article belongs to the Special Issue Sustainable Management of Water and Environment with the Aid of Advanced Computing Methods)

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21 pages, 51640 KiB

Open AccessArticle

Application of Harris Hawks Optimization with Reservoir Simulation Model Considering Hedging Rule for Network Reservoir System

by Rapeepat Techarungruengsakul and Anongrit Kangrang

Sustainability 2022, 14(9), 4913; https://doi.org/10.3390/su14094913 - 19 Apr 2022

Cited by 15 | Viewed by 2472

Abstract

This research aims to apply the Harris hawks optimization (HHO) technique connected with a reservoir simulation model to search optimal rule curves of the network reservoir system in Thailand. The downstream water demand from the network reservoir that required shared water discharge, hydrological data, and physical data were considered in the reservoir simulation model. A comparison of the situation of water shortage using optimal rule curves from HHO technique, genetic algorithm (GA), and wind-driven optimization (WDO) is presented. The results showed that the new rule curves derived from the HHO technique with network reservoir searching were able to alleviate the water shortage and over-flow situations better than the current rule curves. The efficiency of using rule curves from HHO technique compared to GA and WDO techniques showed that the HHO technique can provide a better solution that reduced water scarcity and average over-flow compared with the current rule curves by up to 4.80%, 4.70%, and 4.50%, respectively. In addition, HHO was efficient in converging rule curve solutions faster than GA and WDO techniques by 15.00% and 54.00%, respectively. In conclusion, the HHO technique can be used to search for optimal network reservoir rule curves solutions effectively. Full article

(This article belongs to the Special Issue Sustainable Management of Water and Environment with the Aid of Advanced Computing Methods)

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37 pages, 9248 KiB

Open AccessArticle

A New Methodology for Reference Evapotranspiration Prediction and Uncertainty Analysis under Climate Change Conditions Based on Machine Learning, Multi Criteria Decision Making and Monte Carlo Methods

by Mojtaba Kadkhodazadeh, Mahdi Valikhan Anaraki, Amirreza Morshed-Bozorgdel and Saeed Farzin

Sustainability 2022, 14(5), 2601; https://doi.org/10.3390/su14052601 - 23 Feb 2022

Cited by 54 | Viewed by 5964

Abstract

In the present study, a new methodology for reference evapotranspiration (ETo) prediction and uncertainty analysis under climate change and COVID-19 post-pandemic recovery scenarios for the period 2021–2050 at nine stations in the two basins of Lake Urmia and Sefidrood is presented. For this purpose, firstly ETo data were estimated using meteorological data and the FAO Penman–Monteith (FAO-56 PM) method. Then, ETo modeling by six machine learning techniques including multiple linear regression (MLR), multiple non-linear regression (MNLR), multivariate adaptive regression splines (MARS), model tree M5 (M5), random forest (RF) and least-squares boost (LSBoost) was carried out. The technique for order of preference by similarity to ideal solution (TOPSIS) method was used under seven scenarios to rank models with evaluation and time criteria in the next step. After proving the acceptable performance of the LSBoost model, the downscaling of temperature (T) and precipitation (P) by the delta change factor (CF) method under three models ACCESS-ESM1-5, CanESM5 and MRI-ESM2-0 (scenarios SSP245-cov-fossil (SCF), SSP245-cov-modgreen (SCM) and SSP245-cov-strgreen (SCS)) was performed. The results showed that the monthly changes in the average T increases at all stations for all scenarios. Also, the average monthly change ratio of P increases in most stations and scenarios. In the next step, ETo forecasting under climate change for periods (2021–2050) was performed using the best model. Prediction results showed that ETo increases in all scenarios and stations in a pessimistic and optimistic state. In addition, the Monte Carlo method (MCM) showed that the lowest uncertainty is related to the Mianeh station in the MRI-ESM2-0 model and SCS scenario. Full article

(This article belongs to the Special Issue Sustainable Management of Water and Environment with the Aid of Advanced Computing Methods)

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