Pub Date : 2023-10-20DOI: 10.1007/s11269-023-03642-6
K. A. Jariwala, P. G. Agnihotri
{"title":"Comparative Analysis of Drought Modeling and Forecasting Using Soft Computing Techniques","authors":"K. A. Jariwala, P. G. Agnihotri","doi":"10.1007/s11269-023-03642-6","DOIUrl":"https://doi.org/10.1007/s11269-023-03642-6","url":null,"abstract":"","PeriodicalId":23611,"journal":{"name":"Water Resources Management","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135569914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-17DOI: 10.1007/s11269-023-03630-w
Jing Feng, Yuanyuan Yang, Jianzhu Li
{"title":"Optimization of the Low-Impact Development Facility Area Based on a Surrogate Model","authors":"Jing Feng, Yuanyuan Yang, Jianzhu Li","doi":"10.1007/s11269-023-03630-w","DOIUrl":"https://doi.org/10.1007/s11269-023-03630-w","url":null,"abstract":"","PeriodicalId":23611,"journal":{"name":"Water Resources Management","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136033637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-16DOI: 10.1007/s11269-023-03641-7
Sobhy R. Emara, Tamer A. Gado, Bakenaz A. Zeidan, Asaad M. Armanuos
Abstract Subsurface physical barriers have been effectively used to mitigate seawater intrusion (SWI). Traditionally, the primary emphasis in both numerical studies and practical implementations has been on vertical barriers. The current research aims to explore the dynamics of SWI under various cutoff-wall inclination angles and depths, as well as aquifer heterogeneity using both experimental and numerical simulations. The impact of aquifer characteristics was assessed by utilizing a low hydraulic conductivity (K) aquifer (case L), a high hydraulic conductivity aquifer (case H), and two stratified aquifers. The stratified aquifers were created by grouping different hydraulic conductivity layers into two cases: high K above low K (case H/L) and low K above high K (case L/H). The model simulations covered seven different cutoff-wall inclination angles: 45.0°, 63.4°, 76.0°, 90.0°, 104.0°, 116.6°, and 135.0°. The maximum repulsion ratio of SWI wedge length was observed at an inclination angle of 76.0° for cutoff-wall depth ratios up to 0.623. However, as the depth ratio increased to 0.811, the maximum repulsion ratio shifted to an angle of 63.4° for all aquifers studied. At an inclined cutoff depth ratio of 0.811, the cutoff-wall inclination angle of 45.0° had the most significant impact on the saltwater wedge area. This results in SWI area reductions of 74.9%, 79.8%, 74.7%, and 62.6% for case L, case H, case H/L, and case L/H, respectively. This study provides practical insights into the prevention of SWI. Nevertheless, a thorough cost–benefit analysis is necessary to assess the feasibility of constructing inclined cutoff-walls.
{"title":"Evaluating the Impact of Inclined Cutoff-Wall to Control Seawater Intrusion in Heterogeneous Coastal Aquifers","authors":"Sobhy R. Emara, Tamer A. Gado, Bakenaz A. Zeidan, Asaad M. Armanuos","doi":"10.1007/s11269-023-03641-7","DOIUrl":"https://doi.org/10.1007/s11269-023-03641-7","url":null,"abstract":"Abstract Subsurface physical barriers have been effectively used to mitigate seawater intrusion (SWI). Traditionally, the primary emphasis in both numerical studies and practical implementations has been on vertical barriers. The current research aims to explore the dynamics of SWI under various cutoff-wall inclination angles and depths, as well as aquifer heterogeneity using both experimental and numerical simulations. The impact of aquifer characteristics was assessed by utilizing a low hydraulic conductivity (K) aquifer (case L), a high hydraulic conductivity aquifer (case H), and two stratified aquifers. The stratified aquifers were created by grouping different hydraulic conductivity layers into two cases: high K above low K (case H/L) and low K above high K (case L/H). The model simulations covered seven different cutoff-wall inclination angles: 45.0°, 63.4°, 76.0°, 90.0°, 104.0°, 116.6°, and 135.0°. The maximum repulsion ratio of SWI wedge length was observed at an inclination angle of 76.0° for cutoff-wall depth ratios up to 0.623. However, as the depth ratio increased to 0.811, the maximum repulsion ratio shifted to an angle of 63.4° for all aquifers studied. At an inclined cutoff depth ratio of 0.811, the cutoff-wall inclination angle of 45.0° had the most significant impact on the saltwater wedge area. This results in SWI area reductions of 74.9%, 79.8%, 74.7%, and 62.6% for case L, case H, case H/L, and case L/H, respectively. This study provides practical insights into the prevention of SWI. Nevertheless, a thorough cost–benefit analysis is necessary to assess the feasibility of constructing inclined cutoff-walls.","PeriodicalId":23611,"journal":{"name":"Water Resources Management","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136114052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-14DOI: 10.1007/s11269-023-03633-7
Efsun Bacaksız, Mücahit Opan, Zuhal Elif Kara Dilek, Murat Karadeniz
{"title":"Evaluation of Optimal Energy Productıon Usıng Deterministic, Probabilistic and Risky Cases In a Multi-Reservoir System","authors":"Efsun Bacaksız, Mücahit Opan, Zuhal Elif Kara Dilek, Murat Karadeniz","doi":"10.1007/s11269-023-03633-7","DOIUrl":"https://doi.org/10.1007/s11269-023-03633-7","url":null,"abstract":"","PeriodicalId":23611,"journal":{"name":"Water Resources Management","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135802064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-13DOI: 10.1007/s11269-023-03617-7
Andrea D’Aniello
Abstract The use of fiber optic distributed temperature sensing (DTS) to detect and locate leaks is still in its infancy in water infrastructure, despite its promising capabilities. Only few experiments tested this technology, and none of these studies focused on small but persistent leaks, like background leakages, which are ubiquitous and generally go undetected with the technology currently available, thus posing a serious threat to the available water resource. To test the feasibility of detecting and locating background leakages with fiber optic DTS, this study provides a detailed analysis on flow and temperature alterations around leaking water pipelines in presence of small leaks (5, 25, and 125 L/d) with small to moderate temperature differences with the surrounding soil, under 3 different pipe defect configurations, either in absence or in presence of pipe thermal insulation. Transient 3D heat transfer-unsaturated flow numerical simulations showed that there is potential to use temperature alterations to detect and locate incredibly small leaks with fiber optic DTS, like background leakages, despite the influence of pipe temperature on the surrounding soil. The analysis showed that extent, distribution, and magnitude of these alterations are convection dominated at a given temperature difference between leaked water and undisturbed soil, and that it may not be strictly necessary to place the optical fiber directly below the pipe. Indeed, optical fibers located within the utility trench at the sides of the pipe and below its bottom showed comparable or even better performance, thus giving new opportunities to retrofit existing pipelines as well.
{"title":"Detecting Background Leakages in Water Infrastructure With Fiber Optic Distributed Temperature Sensing: Insights From a Heat Transfer-Unsaturated Flow Model","authors":"Andrea D’Aniello","doi":"10.1007/s11269-023-03617-7","DOIUrl":"https://doi.org/10.1007/s11269-023-03617-7","url":null,"abstract":"Abstract The use of fiber optic distributed temperature sensing (DTS) to detect and locate leaks is still in its infancy in water infrastructure, despite its promising capabilities. Only few experiments tested this technology, and none of these studies focused on small but persistent leaks, like background leakages, which are ubiquitous and generally go undetected with the technology currently available, thus posing a serious threat to the available water resource. To test the feasibility of detecting and locating background leakages with fiber optic DTS, this study provides a detailed analysis on flow and temperature alterations around leaking water pipelines in presence of small leaks (5, 25, and 125 L/d) with small to moderate temperature differences with the surrounding soil, under 3 different pipe defect configurations, either in absence or in presence of pipe thermal insulation. Transient 3D heat transfer-unsaturated flow numerical simulations showed that there is potential to use temperature alterations to detect and locate incredibly small leaks with fiber optic DTS, like background leakages, despite the influence of pipe temperature on the surrounding soil. The analysis showed that extent, distribution, and magnitude of these alterations are convection dominated at a given temperature difference between leaked water and undisturbed soil, and that it may not be strictly necessary to place the optical fiber directly below the pipe. Indeed, optical fibers located within the utility trench at the sides of the pipe and below its bottom showed comparable or even better performance, thus giving new opportunities to retrofit existing pipelines as well.","PeriodicalId":23611,"journal":{"name":"Water Resources Management","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135858103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-13DOI: 10.1007/s11269-023-03609-7
Han Cao, Jun Qiu, Hui-Min Zuo, Fang-Fang Li
{"title":"A Long-Term Operational Scheme for Hybrid Hydro-Photovoltaic (PV) Systems that Considers the Uncertainties in Reservoir Inflow and Solar Radiation Based on Scenario Trees","authors":"Han Cao, Jun Qiu, Hui-Min Zuo, Fang-Fang Li","doi":"10.1007/s11269-023-03609-7","DOIUrl":"https://doi.org/10.1007/s11269-023-03609-7","url":null,"abstract":"","PeriodicalId":23611,"journal":{"name":"Water Resources Management","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135858164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-12DOI: 10.1007/s11269-023-03616-8
Aleska Kaufmann Almeida, Isabel Kaufmann de Almeida, José Antonio Guarienti, Luiz Felipe Finck, Sandra Garcia Gabas
{"title":"Time of Concentration Model for Non-Urban Tropical Basins Based on Physiographic Characteristics and Observed Rainfall Responses","authors":"Aleska Kaufmann Almeida, Isabel Kaufmann de Almeida, José Antonio Guarienti, Luiz Felipe Finck, Sandra Garcia Gabas","doi":"10.1007/s11269-023-03616-8","DOIUrl":"https://doi.org/10.1007/s11269-023-03616-8","url":null,"abstract":"","PeriodicalId":23611,"journal":{"name":"Water Resources Management","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135967732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-12DOI: 10.1007/s11269-023-03638-2
Nadire Ucler, Hale Gonce Kocken
{"title":"A Scenario-based Interval Multi-objective Mixed-integer Programming Model for a Water Supply Problem: An Integrated AHP Technique","authors":"Nadire Ucler, Hale Gonce Kocken","doi":"10.1007/s11269-023-03638-2","DOIUrl":"https://doi.org/10.1007/s11269-023-03638-2","url":null,"abstract":"","PeriodicalId":23611,"journal":{"name":"Water Resources Management","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135963939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Researchers and engineers employ machine learning (ML) tools to detect pipe bursts and prevent significant non-revenue water losses in water distribution systems (WDS). Nonetheless, many approaches developed so far consider a fixed number of sensors, which requires the ML model redevelopment and collection of sufficient data with the new sensor configuration for training. To overcome these issues, this study presents a novel approach based on Long Short-Term Memory neural networks (NNs) that leverages transfer learning to manage a varying number of sensors and retain good detection performance with limited training data. The proposed detection model first learns to reproduce the normal behavior of the system on a dataset obtained in burst-free conditions. The training process involves predicting flow and pressure one-time step ahead using historical data and time-related features as inputs. During testing, a post-prediction step flags potential bursts based on the comparison between the observations and model predictions using a time-varied error threshold. When adding new sensors, we implement transfer learning by replicating the weights of existing channels and then fine-tune the augmented NN. We evaluate the robustness of the methodology on simulated fire hydrant bursts and real-bursts in 10 district metered areas (DMAs) of the UK. For real bursts, we perform a sensitivity analysis to understand the impact of data resolution and error threshold on burst detection performance. The results obtained demonstrate that this ML-based methodology can achieve Precision of up to 98.1% in real-life settings and can identify bursts, even in data scarce conditions.
{"title":"Leveraging Transfer Learning in LSTM Neural Networks for Data-Efficient Burst Detection in Water Distribution Systems","authors":"Konstantinos Glynis, Zoran Kapelan, Martijn Bakker, Riccardo Taormina","doi":"10.1007/s11269-023-03637-3","DOIUrl":"https://doi.org/10.1007/s11269-023-03637-3","url":null,"abstract":"Abstract Researchers and engineers employ machine learning (ML) tools to detect pipe bursts and prevent significant non-revenue water losses in water distribution systems (WDS). Nonetheless, many approaches developed so far consider a fixed number of sensors, which requires the ML model redevelopment and collection of sufficient data with the new sensor configuration for training. To overcome these issues, this study presents a novel approach based on Long Short-Term Memory neural networks (NNs) that leverages transfer learning to manage a varying number of sensors and retain good detection performance with limited training data. The proposed detection model first learns to reproduce the normal behavior of the system on a dataset obtained in burst-free conditions. The training process involves predicting flow and pressure one-time step ahead using historical data and time-related features as inputs. During testing, a post-prediction step flags potential bursts based on the comparison between the observations and model predictions using a time-varied error threshold. When adding new sensors, we implement transfer learning by replicating the weights of existing channels and then fine-tune the augmented NN. We evaluate the robustness of the methodology on simulated fire hydrant bursts and real-bursts in 10 district metered areas (DMAs) of the UK. For real bursts, we perform a sensitivity analysis to understand the impact of data resolution and error threshold on burst detection performance. The results obtained demonstrate that this ML-based methodology can achieve Precision of up to 98.1% in real-life settings and can identify bursts, even in data scarce conditions.","PeriodicalId":23611,"journal":{"name":"Water Resources Management","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136012558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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