Abstract Multi-outlet pipes can be used to distribute and collect fluids and have applications in various engineering fields, especially in the water supply system. The hydraulic design of a submain unit pipeline with multiple outlets is a very important concern for the proper hydraulic performance of irrigation water distribution systems. The operating inlet pressure head, H0I, is a main hydraulic component for the proper hydraulically efficient design and evaluation of pressure head distribution along the line. The energy-gradient ratio (EGR) approach is a useful tool to identify first which type of pressure profile occurs for a given uniform design slope with other hydraulic variables initially known and then, comprehensively evaluate its definite hydraulic characteristics along the line. Knowing the hydraulic properties of any type of pressure profile regarded enables the design engineer to evaluate pressure parameters through the line sections in a simple way. The procedure is simplified by regarding the localized head loss along the pipe but neglecting the change in kinetic head. The present analytical technique performs sufficiently accurate in comparison with the computer-aided software design technique, for all performed simulations.
{"title":"Operating inlet pressure head assessment for multi-outlet submain manifolds in a low-pressurized water distribution network system","authors":"Gürol Yıldırım","doi":"10.2166/ws.2023.280","DOIUrl":"https://doi.org/10.2166/ws.2023.280","url":null,"abstract":"Abstract Multi-outlet pipes can be used to distribute and collect fluids and have applications in various engineering fields, especially in the water supply system. The hydraulic design of a submain unit pipeline with multiple outlets is a very important concern for the proper hydraulic performance of irrigation water distribution systems. The operating inlet pressure head, H0I, is a main hydraulic component for the proper hydraulically efficient design and evaluation of pressure head distribution along the line. The energy-gradient ratio (EGR) approach is a useful tool to identify first which type of pressure profile occurs for a given uniform design slope with other hydraulic variables initially known and then, comprehensively evaluate its definite hydraulic characteristics along the line. Knowing the hydraulic properties of any type of pressure profile regarded enables the design engineer to evaluate pressure parameters through the line sections in a simple way. The procedure is simplified by regarding the localized head loss along the pipe but neglecting the change in kinetic head. The present analytical technique performs sufficiently accurate in comparison with the computer-aided software design technique, for all performed simulations.","PeriodicalId":23573,"journal":{"name":"Water Science & Technology: Water Supply","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136318302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Effective control of water leakage is a critical aspect for ensuring the high-quality development of the water sector. In recent years, the utilization of sniffer dogs in water leakage detection has emerged as a promising technology, progressing from laboratory experiments to small-scale real-world applications. Leveraging their remarkable ability to trace chlorine, sniffer dogs have demonstrated an impressive accuracy and high efficiency in detecting underground pipe leaks. This mini-review comprehensively examines the advancements in sniffer dog usage for leak detection, provides an overview of various application methods, and elucidates the conditions and limitations associated with each approach. It also delves into the management mechanisms of sniffer dogs, offering a comparative analysis of different management models. Lastly, this review addresses the challenges inherent in applying sniffer dogs to water leak detection, poses pertinent research questions concerning sniffer dogs' training and the expansion of odour fingerprint, and presents a forward-looking perspective on the subject.
{"title":"Sniffer dogs as an emerging approach for water leakage detection","authors":"Jiazhi Zhong, Dongdong Chen","doi":"10.2166/ws.2023.284","DOIUrl":"https://doi.org/10.2166/ws.2023.284","url":null,"abstract":"Abstract Effective control of water leakage is a critical aspect for ensuring the high-quality development of the water sector. In recent years, the utilization of sniffer dogs in water leakage detection has emerged as a promising technology, progressing from laboratory experiments to small-scale real-world applications. Leveraging their remarkable ability to trace chlorine, sniffer dogs have demonstrated an impressive accuracy and high efficiency in detecting underground pipe leaks. This mini-review comprehensively examines the advancements in sniffer dog usage for leak detection, provides an overview of various application methods, and elucidates the conditions and limitations associated with each approach. It also delves into the management mechanisms of sniffer dogs, offering a comparative analysis of different management models. Lastly, this review addresses the challenges inherent in applying sniffer dogs to water leak detection, poses pertinent research questions concerning sniffer dogs' training and the expansion of odour fingerprint, and presents a forward-looking perspective on the subject.","PeriodicalId":23573,"journal":{"name":"Water Science & Technology: Water Supply","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136235422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Youming Li, Jia Qu, Haosen Zhang, Yan Long, Shu Li
Abstract To meet the demand of accurate water level prediction of the reservoir in Liuxihe River Basin, this paper proposes an improved long short-term memory (LSTM) neural network based on the Bayesian optimization algorithm and wavelet decomposition coupling. Based on the improved model, the water levels of Liuxihe Reservoir and Huanglongdai Reservoir are simulated and predicted by the 1 h prediction length, and the prediction accuracy of the improved model is verified separately by the 3, 6 and 12 h prediction lengths. The results show that: first, Bayesian optimization coupling can significantly reduce the average absolute error and root mean square error of the model and improve the overall prediction accuracy, but this algorithm is insufficient in the optimization of model extremum; Wavelet decomposition coupling can significantly reduce the outliers in model prediction and improve the accuracy of extremum, but it plays relatively weaker role in the overall optimization of the model. Second, by the prediction lengths of 1, 3, 6 and 12 h, the improved model based on the LSTM neural network and coupled with Bayesian optimization and wavelet decomposition is superior to Bayesian optimization and wavelet decomposition coupling model in overall prediction accuracy and prediction accuracy of extremum.
{"title":"Water level prediction of Liuxihe Reservoir based on improved long short-term memory neural network","authors":"Youming Li, Jia Qu, Haosen Zhang, Yan Long, Shu Li","doi":"10.2166/ws.2023.282","DOIUrl":"https://doi.org/10.2166/ws.2023.282","url":null,"abstract":"Abstract To meet the demand of accurate water level prediction of the reservoir in Liuxihe River Basin, this paper proposes an improved long short-term memory (LSTM) neural network based on the Bayesian optimization algorithm and wavelet decomposition coupling. Based on the improved model, the water levels of Liuxihe Reservoir and Huanglongdai Reservoir are simulated and predicted by the 1 h prediction length, and the prediction accuracy of the improved model is verified separately by the 3, 6 and 12 h prediction lengths. The results show that: first, Bayesian optimization coupling can significantly reduce the average absolute error and root mean square error of the model and improve the overall prediction accuracy, but this algorithm is insufficient in the optimization of model extremum; Wavelet decomposition coupling can significantly reduce the outliers in model prediction and improve the accuracy of extremum, but it plays relatively weaker role in the overall optimization of the model. Second, by the prediction lengths of 1, 3, 6 and 12 h, the improved model based on the LSTM neural network and coupled with Bayesian optimization and wavelet decomposition is superior to Bayesian optimization and wavelet decomposition coupling model in overall prediction accuracy and prediction accuracy of extremum.","PeriodicalId":23573,"journal":{"name":"Water Science & Technology: Water Supply","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136263577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Fresh water is vital for the survival of life. Rivers are the primary source of freshwater supply. However, over the past few decades, challenges concerning the sustainability of rivers and maintaining their water quality have become countless. Due to rapid and unrestrained advancements, the river's ecosystem gets imbalanced. To assess and predict the water quality from the real data collected, it becomes necessary to devise ways to interpret and analyze the data efficiently. The present work deals with the development of a water quality index based on a fuzzy approach for predicting the water quality of the river Yamuna. The utmost contaminated stretch of the river through Delhi has been taken up for this study. The proposed methodology is elementary, simple, effective, and flexible in assimilating uncertainties involved in complex water management problems. The suggested index involves the most dominant parameters and can act as a practical tool for routine water quality assessment. The outcomes of the study give pronounced facts to water authorities about the awful condition of the river Yamuna in Delhi.
{"title":"Development of a fuzzy-based approach for assessing water quality","authors":"Sumita Gulati, Anshul Bansal, Ashok Pal","doi":"10.2166/ws.2023.279","DOIUrl":"https://doi.org/10.2166/ws.2023.279","url":null,"abstract":"Abstract Fresh water is vital for the survival of life. Rivers are the primary source of freshwater supply. However, over the past few decades, challenges concerning the sustainability of rivers and maintaining their water quality have become countless. Due to rapid and unrestrained advancements, the river's ecosystem gets imbalanced. To assess and predict the water quality from the real data collected, it becomes necessary to devise ways to interpret and analyze the data efficiently. The present work deals with the development of a water quality index based on a fuzzy approach for predicting the water quality of the river Yamuna. The utmost contaminated stretch of the river through Delhi has been taken up for this study. The proposed methodology is elementary, simple, effective, and flexible in assimilating uncertainties involved in complex water management problems. The suggested index involves the most dominant parameters and can act as a practical tool for routine water quality assessment. The outcomes of the study give pronounced facts to water authorities about the awful condition of the river Yamuna in Delhi.","PeriodicalId":23573,"journal":{"name":"Water Science & Technology: Water Supply","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135111716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nazanin Yousefian, Naser Shahnoushi, Ali Firoozzare, Saleh Taghvaeian
The Fariman-Torbat Jam Plain has been under high pressure due to the imbalance of groundwater caused by excessive water extraction. Current conditions necessitate the preservation of appropriate extraction methods. Although the revival of the Qanat is known to be a highly compatible method, its high cost poses a challenge. Identifying and prioritizing measures to revive the Qanat can be a critical factor in managing this challenge. This study identified and prioritized the measures of the revival of the Qanat by using questionnaires, interviews with experts, and the entropy–VIKOR method. The results showed that among the 10 measures identified, reducing the extraction of water and changing the cultivation pattern were the top two priorities, and other measures were placed next. By implementing these measures, it is possible to partially respond to the demands of the natives while preventing further deterioration of the consequences that endanger the plain. Furthermore, considering the fact that the implementation of any measure can be beneficial, they should be determined based on region characteristics and then implemented in order of priority to obtain more favorable results.
{"title":"The prioritization of volunteering revival measures of the Qanat","authors":"Nazanin Yousefian, Naser Shahnoushi, Ali Firoozzare, Saleh Taghvaeian","doi":"10.2166/ws.2023.277","DOIUrl":"https://doi.org/10.2166/ws.2023.277","url":null,"abstract":"The Fariman-Torbat Jam Plain has been under high pressure due to the imbalance of groundwater caused by excessive water extraction. Current conditions necessitate the preservation of appropriate extraction methods. Although the revival of the Qanat is known to be a highly compatible method, its high cost poses a challenge. Identifying and prioritizing measures to revive the Qanat can be a critical factor in managing this challenge. This study identified and prioritized the measures of the revival of the Qanat by using questionnaires, interviews with experts, and the entropy–VIKOR method. The results showed that among the 10 measures identified, reducing the extraction of water and changing the cultivation pattern were the top two priorities, and other measures were placed next. By implementing these measures, it is possible to partially respond to the demands of the natives while preventing further deterioration of the consequences that endanger the plain. Furthermore, considering the fact that the implementation of any measure can be beneficial, they should be determined based on region characteristics and then implemented in order of priority to obtain more favorable results.","PeriodicalId":23573,"journal":{"name":"Water Science & Technology: Water Supply","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135169349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Allocation of water resources is an interesting research topic and one of the main challenges of arid regions. From the point of view of agriculture, this issue is closely related to ecological balance, economic development and social stability. Therefore, fair, efficient and sustainable allocation of water resources for users and decision-making is essential. This paper presents a dynamic stochastic programming model that predicts soil moisture content in a growing season based on data collected from an experimental farm. The model included three types of loam soil, silt loam and clay loam with three treatments of irrigation intervals of 3, 7, 10 and 14 days and three amounts of water allocation with three replications. The proposed framework was evaluated with two criteria of spatial and temporal equity, and the optimal water allocation was analyzed based on this criterion. The results showed that the criterion of temporal equity for loam soil with 7-day irrigation intervals is more than twice that of 14-day irrigation intervals. In addition, the depth of irrigation has had the greatest impact on the fluctuations of the criterion of equity in water allocation in the growing season.
{"title":"Sustainable ecosystem management based on optimal water allocation and equity measures","authors":"Guo Li","doi":"10.2166/ws.2023.276","DOIUrl":"https://doi.org/10.2166/ws.2023.276","url":null,"abstract":"Abstract Allocation of water resources is an interesting research topic and one of the main challenges of arid regions. From the point of view of agriculture, this issue is closely related to ecological balance, economic development and social stability. Therefore, fair, efficient and sustainable allocation of water resources for users and decision-making is essential. This paper presents a dynamic stochastic programming model that predicts soil moisture content in a growing season based on data collected from an experimental farm. The model included three types of loam soil, silt loam and clay loam with three treatments of irrigation intervals of 3, 7, 10 and 14 days and three amounts of water allocation with three replications. The proposed framework was evaluated with two criteria of spatial and temporal equity, and the optimal water allocation was analyzed based on this criterion. The results showed that the criterion of temporal equity for loam soil with 7-day irrigation intervals is more than twice that of 14-day irrigation intervals. In addition, the depth of irrigation has had the greatest impact on the fluctuations of the criterion of equity in water allocation in the growing season.","PeriodicalId":23573,"journal":{"name":"Water Science & Technology: Water Supply","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135315602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract At present, water rights trading is used by many countries around the world to solve the problems of water resource shortage and uneven spatial and temporal distribution. However, there is no scientific method for setting the term of water rights trading, which is generally determined through negotiation between the trading parties. In order to find a more objective method for determining the term of water rights trading, we constructed a loss–benefit model about the term of water rights trading and solved it based on the principle of comprehensive benefits greater than zero to determine the optimal term of water rights trading. First, we analyzed the benefits and losses brought by water rights trading, then constructed a loss–benefit function with trading term as the independent variable. Second, based on the graphical analysis method, we analyzed the benefits and losses of water rights trading. Finally, the optimal term for water rights trading is determined based on the loss–benefit function and combined with a graphical analysis method. In addition, this study can also help us determine the longest or shortest water rights trading term based on actual situations.
{"title":"A method for setting the term of water rights trading based on loss–benefit function","authors":"Hao Xu, Liuxin Chen, Qiongfang Li","doi":"10.2166/ws.2023.278","DOIUrl":"https://doi.org/10.2166/ws.2023.278","url":null,"abstract":"Abstract At present, water rights trading is used by many countries around the world to solve the problems of water resource shortage and uneven spatial and temporal distribution. However, there is no scientific method for setting the term of water rights trading, which is generally determined through negotiation between the trading parties. In order to find a more objective method for determining the term of water rights trading, we constructed a loss–benefit model about the term of water rights trading and solved it based on the principle of comprehensive benefits greater than zero to determine the optimal term of water rights trading. First, we analyzed the benefits and losses brought by water rights trading, then constructed a loss–benefit function with trading term as the independent variable. Second, based on the graphical analysis method, we analyzed the benefits and losses of water rights trading. Finally, the optimal term for water rights trading is determined based on the loss–benefit function and combined with a graphical analysis method. In addition, this study can also help us determine the longest or shortest water rights trading term based on actual situations.","PeriodicalId":23573,"journal":{"name":"Water Science & Technology: Water Supply","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135316273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shaolei Guo, Shifeng Sun, Xianqi Zhang, Haiyang Chen, Haiyang Li
Abstract Precipitation prediction is one of the important issues in meteorology and hydrology, and it is of great significance for water resources management, flood control, and disaster reduction. In this paper, a precipitation prediction model based on the empirical mode decomposition–variational mode decomposition–long short-term memory (EMD–VMD–LSTM) is proposed. This model is coupled with EMD, VMD, and LSTM to improve the accuracy and reliability of precipitation prediction by using the characteristics of EMD for noise removal, VMD for trend extraction, and LSTM for long-term memory. The monthly precipitation data from 2000 to 2019 in Luoyang City, Henan Province, China, are selected as the research object. This model is compared with the standalone LSTM model, EMD–LSTM coupled model, and VMD–LSTM coupled model. The research results show that the maximum relative error and minimum relative error of the precipitation prediction using the EMD–VMD–LSTM neural network coupled model are 9.64 and −7.52%, respectively, with a 100% prediction accuracy. This coupled model has better accuracy than the other three models in predicting precipitation in Luoyang City. In summary, the proposed EMD–VMD–LSTM precipitation prediction model combines the advantages of multiple methods and provides an effective way to predict precipitation.
{"title":"Monthly precipitation prediction based on the EMD–VMD–LSTM coupled model","authors":"Shaolei Guo, Shifeng Sun, Xianqi Zhang, Haiyang Chen, Haiyang Li","doi":"10.2166/ws.2023.275","DOIUrl":"https://doi.org/10.2166/ws.2023.275","url":null,"abstract":"Abstract Precipitation prediction is one of the important issues in meteorology and hydrology, and it is of great significance for water resources management, flood control, and disaster reduction. In this paper, a precipitation prediction model based on the empirical mode decomposition–variational mode decomposition–long short-term memory (EMD–VMD–LSTM) is proposed. This model is coupled with EMD, VMD, and LSTM to improve the accuracy and reliability of precipitation prediction by using the characteristics of EMD for noise removal, VMD for trend extraction, and LSTM for long-term memory. The monthly precipitation data from 2000 to 2019 in Luoyang City, Henan Province, China, are selected as the research object. This model is compared with the standalone LSTM model, EMD–LSTM coupled model, and VMD–LSTM coupled model. The research results show that the maximum relative error and minimum relative error of the precipitation prediction using the EMD–VMD–LSTM neural network coupled model are 9.64 and −7.52%, respectively, with a 100% prediction accuracy. This coupled model has better accuracy than the other three models in predicting precipitation in Luoyang City. In summary, the proposed EMD–VMD–LSTM precipitation prediction model combines the advantages of multiple methods and provides an effective way to predict precipitation.","PeriodicalId":23573,"journal":{"name":"Water Science & Technology: Water Supply","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135315732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Flooding occurs frequently compared to other natural disasters. Less developed countries are severely affected by floods. This research provides an integrated hydrometeorological system that forecasts hourly reservoir inflows using a full physically based rainfall–runoff and numerical weather models. This study develops a 5-day lead time reservoir inflow prediction using TIGGE ensemble datasets from ECMWF, UKMO, and NCEP for the Dharoi Dam in Gujarat, India. The ensemble data were post-processed using censored non-homogeneous Linear Regression and Bayesian model averaging approach. These post-processed data were used in a hydrological model to simulate hydrological processes and predict Dharoi Dam reservoir inflows. Results show that ECMWF with a BMA approach and HEC-HMS hydrological model can predict reservoir inflows in the Sabarmati River basin. The correlation result of an observed reservoir inflow is 0.91. This research can help regional water resource managers and government officials to plan and manage water resources.
{"title":"Improving the reservoir inflow prediction using TIGGE ensemble data and hydrological model for Dharoi Dam, India","authors":"Anant Patel, S. M. Yadav","doi":"10.2166/ws.2023.274","DOIUrl":"https://doi.org/10.2166/ws.2023.274","url":null,"abstract":"Abstract Flooding occurs frequently compared to other natural disasters. Less developed countries are severely affected by floods. This research provides an integrated hydrometeorological system that forecasts hourly reservoir inflows using a full physically based rainfall–runoff and numerical weather models. This study develops a 5-day lead time reservoir inflow prediction using TIGGE ensemble datasets from ECMWF, UKMO, and NCEP for the Dharoi Dam in Gujarat, India. The ensemble data were post-processed using censored non-homogeneous Linear Regression and Bayesian model averaging approach. These post-processed data were used in a hydrological model to simulate hydrological processes and predict Dharoi Dam reservoir inflows. Results show that ECMWF with a BMA approach and HEC-HMS hydrological model can predict reservoir inflows in the Sabarmati River basin. The correlation result of an observed reservoir inflow is 0.91. This research can help regional water resource managers and government officials to plan and manage water resources.","PeriodicalId":23573,"journal":{"name":"Water Science & Technology: Water Supply","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135884927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The exploitation of water resources to provide water for agriculture follows methods that increase the efficiency of adopted policies. One of the effective ways to improve the efficiency of these systems is to evaluate the role of flow estimation in improving performance indicators such as reliability and vulnerability. In the correlation of water, energy and food, the purpose of decision-making is to achieve a balance between water extraction and energy consumption, which will lead to a reduction in the risk of supplying the water needed by the plant during periods of drought stress. In this article, a decision-making method using discrete wavelet transform and primal-dual algorithm is introduced to estimate the amount of monthly groundwater extraction. The proposed model has been evaluated by the Nash–Sutcliffe method and the mean squared error and optimized to increase the reliability of agricultural water supply. The results indicate the strong role of the accuracy of the proposed method in the efficiency of the aforementioned policies, as it has shown an 8% increase in reliability.
{"title":"Developing a primal-dual algorithm for optimal planning of groundwater resources","authors":"Bohong Zheng, Yuanyuan Huang","doi":"10.2166/ws.2023.273","DOIUrl":"https://doi.org/10.2166/ws.2023.273","url":null,"abstract":"Abstract The exploitation of water resources to provide water for agriculture follows methods that increase the efficiency of adopted policies. One of the effective ways to improve the efficiency of these systems is to evaluate the role of flow estimation in improving performance indicators such as reliability and vulnerability. In the correlation of water, energy and food, the purpose of decision-making is to achieve a balance between water extraction and energy consumption, which will lead to a reduction in the risk of supplying the water needed by the plant during periods of drought stress. In this article, a decision-making method using discrete wavelet transform and primal-dual algorithm is introduced to estimate the amount of monthly groundwater extraction. The proposed model has been evaluated by the Nash–Sutcliffe method and the mean squared error and optimized to increase the reliability of agricultural water supply. The results indicate the strong role of the accuracy of the proposed method in the efficiency of the aforementioned policies, as it has shown an 8% increase in reliability.","PeriodicalId":23573,"journal":{"name":"Water Science & Technology: Water Supply","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136033073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：