Water Resources Management最新文献_第8页

Comparative Analysis of Drought Modeling and Forecasting Using Soft Computing Techniques 基于软计算技术的干旱模拟与预报的对比分析

3区环境科学与生态学

Water Resources Management Pub Date : 2023-10-20 DOI: 10.1007/s11269-023-03642-6

K. A. Jariwala, P. G. Agnihotri

引用次数: 0

Optimization of the Low-Impact Development Facility Area Based on a Surrogate Model 基于代理模型的低影响发展设施区域优化

3区环境科学与生态学

Water Resources Management Pub Date : 2023-10-17 DOI: 10.1007/s11269-023-03630-w

Jing Feng, Yuanyuan Yang, Jianzhu Li

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Hybrid Extreme Gradient Boosting and Nonlinear Ensemble Models for Suspended Sediment Load Prediction in an Agricultural Catchment 农业流域悬沙负荷预测的混合极端梯度增强和非线性集合模型

3区环境科学与生态学

Water Resources Management Pub Date : 2023-10-17 DOI: 10.1007/s11269-023-03629-3

Gebre Gelete

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Evaluating the Impact of Inclined Cutoff-Wall to Control Seawater Intrusion in Heterogeneous Coastal Aquifers 非均质沿海含水层倾斜截流墙控制海水入侵效果评价

3区环境科学与生态学

Water Resources Management Pub Date : 2023-10-16 DOI: 10.1007/s11269-023-03641-7

Sobhy R. Emara, Tamer A. Gado, Bakenaz A. Zeidan, Asaad M. Armanuos

{"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}

引用次数: 0

Evaluation of Optimal Energy Productıon Usıng Deterministic, Probabilistic and Risky Cases In a Multi-Reservoir System 多水库系统的确定性、概率和风险情况下的最优能量评价Productıon Usıng

3区环境科学与生态学

Water Resources Management Pub Date : 2023-10-14 DOI: 10.1007/s11269-023-03633-7

Efsun Bacaksız, Mücahit Opan, Zuhal Elif Kara Dilek, Murat Karadeniz

引用次数: 0

Detecting Background Leakages in Water Infrastructure With Fiber Optic Distributed Temperature Sensing: Insights From a Heat Transfer-Unsaturated Flow Model 用光纤分布式温度传感检测水基础设施中的背景泄漏:来自传热-不饱和流动模型的见解

3区环境科学与生态学

Water Resources Management Pub Date : 2023-10-13 DOI: 10.1007/s11269-023-03617-7

Andrea D’Aniello

{"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}

引用次数: 0

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 基于情景树的考虑库流和太阳辐射不确定性的水电光伏混合发电系统长期运行方案

3区环境科学与生态学

Water Resources Management Pub Date : 2023-10-13 DOI: 10.1007/s11269-023-03609-7

Han Cao, Jun Qiu, Hui-Min Zuo, Fang-Fang Li

引用次数: 0

Time of Concentration Model for Non-Urban Tropical Basins Based on Physiographic Characteristics and Observed Rainfall Responses 基于地理特征和观测降水响应的非城市热带盆地浓度模式时间

3区环境科学与生态学

Water Resources Management Pub Date : 2023-10-12 DOI: 10.1007/s11269-023-03616-8

Aleska Kaufmann Almeida, Isabel Kaufmann de Almeida, José Antonio Guarienti, Luiz Felipe Finck, Sandra Garcia Gabas

引用次数: 0

A Scenario-based Interval Multi-objective Mixed-integer Programming Model for a Water Supply Problem: An Integrated AHP Technique 基于场景的供水问题区间多目标混合整数规划模型:一种综合AHP技术

3区环境科学与生态学

Water Resources Management Pub Date : 2023-10-12 DOI: 10.1007/s11269-023-03638-2

Nadire Ucler, Hale Gonce Kocken

引用次数: 0

Leveraging Transfer Learning in LSTM Neural Networks for Data-Efficient Burst Detection in Water Distribution Systems 利用LSTM神经网络中的迁移学习实现配水系统的数据高效突发检测

3区环境科学与生态学

Water Resources Management Pub Date : 2023-10-12 DOI: 10.1007/s11269-023-03637-3

Konstantinos Glynis, Zoran Kapelan, Martijn Bakker, Riccardo Taormina

{"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}

引用次数: 0