Water Science & Technology: Water Supply最新文献_第3页

Operating inlet pressure head assessment for multi-outlet submain manifolds in a low-pressurized water distribution network system 低压配水管网多出口副总管运行入口压头评估

Water Science & Technology: Water Supply Pub Date : 2023-10-27 DOI: 10.2166/ws.2023.280

Gürol Yıldırım

{"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":"46 2","pages":"0"},"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}

引用次数: 0

Sniffer dogs as an emerging approach for water leakage detection 嗅探犬是侦测漏水的新方法

Water Science & Technology: Water Supply Pub Date : 2023-10-27 DOI: 10.2166/ws.2023.284

Jiazhi Zhong, Dongdong Chen

引用次数: 0

Water level prediction of Liuxihe Reservoir based on improved long short-term memory neural network 基于改进型长短期记忆神经网络的柳溪河水库水位预测

Water Science & Technology: Water Supply Pub Date : 2023-10-27 DOI: 10.2166/ws.2023.282

Youming Li, Jia Qu, Haosen Zhang, Yan Long, Shu Li

{"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":"52 1","pages":"0"},"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}

引用次数: 0

Development of a fuzzy-based approach for assessing water quality 发展一种基于模糊的水质评估方法

Water Science & Technology: Water Supply Pub Date : 2023-10-25 DOI: 10.2166/ws.2023.279

Sumita Gulati, Anshul Bansal, Ashok Pal

引用次数: 0

The prioritization of volunteering revival measures of the Qanat 坎儿井志愿复兴的优先化措施

Water Science & Technology: Water Supply Pub Date : 2023-10-25 DOI: 10.2166/ws.2023.277

Nazanin Yousefian, Naser Shahnoushi, Ali Firoozzare, Saleh Taghvaeian

{"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":"68 1","pages":"0"},"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}

引用次数: 0

Sustainable ecosystem management based on optimal water allocation and equity measures 基于水资源优化分配和公平措施的可持续生态系统管理

Water Science & Technology: Water Supply Pub Date : 2023-10-24 DOI: 10.2166/ws.2023.276

Guo Li

{"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":"25 4","pages":"0"},"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}

引用次数: 0

A method for setting the term of water rights trading based on loss–benefit function 一种基于损益函数的水权交易期限设定方法

Water Science & Technology: Water Supply Pub Date : 2023-10-24 DOI: 10.2166/ws.2023.278

Hao Xu, Liuxin Chen, Qiongfang Li

{"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":"1 1","pages":"0"},"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}

引用次数: 0

Monthly precipitation prediction based on the EMD–VMD–LSTM coupled model 基于EMD-VMD-LSTM耦合模式的月降水预测

Water Science & Technology: Water Supply Pub Date : 2023-10-24 DOI: 10.2166/ws.2023.275

Shaolei Guo, Shifeng Sun, Xianqi Zhang, Haiyang Chen, Haiyang Li

{"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":"60 11","pages":"0"},"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}

引用次数: 0

Improving the reservoir inflow prediction using TIGGE ensemble data and hydrological model for Dharoi Dam, India 利用TIGGE集合数据和水文模型改进印度Dharoi大坝入库预测

Water Science & Technology: Water Supply Pub Date : 2023-10-18 DOI: 10.2166/ws.2023.274

Anant Patel, S. M. Yadav

引用次数: 0

Developing a primal-dual algorithm for optimal planning of groundwater resources 地下水资源优化规划的原始-对偶算法研究

Water Science & Technology: Water Supply Pub Date : 2023-10-17 DOI: 10.2166/ws.2023.273

Bohong Zheng, Yuanyuan Huang

引用次数: 0