Water Practice & Technology最新文献_第3页

Chezy's resistance coefficient in vaulted rectangular conduit 拱形矩形导管中的切齐阻力系数

Water Practice & Technology Pub Date : 2024-07-02 DOI: 10.2166/wpt.2024.170

I. Loukam, B. Achour

引用次数: 0

Modeling runoff in Bhima River catchment, India: A comparison of artificial neural networks and empirical models 印度比马河流域的径流建模：人工神经网络与经验模型的比较

Water Practice & Technology Pub Date : 2024-06-14 DOI: 10.2166/wpt.2024.157

Pradip Dalavi, S. R. Bhakar, Jitendra Rajput, Venkatesh Gaddikeri, Ravindra Kumar Tiwari, Abhishek Shukla, Dinesh Kumar Vishwakarma

{"title":"Modeling runoff in Bhima River catchment, India: A comparison of artificial neural networks and empirical models","authors":"Pradip Dalavi, S. R. Bhakar, Jitendra Rajput, Venkatesh Gaddikeri, Ravindra Kumar Tiwari, Abhishek Shukla, Dinesh Kumar Vishwakarma","doi":"10.2166/wpt.2024.157","DOIUrl":"https://doi.org/10.2166/wpt.2024.157","url":null,"abstract":"\u0000 Effective water resource management in gauged catchments relies on accurate runoff prediction. For ungauged catchments, empirical models are used due to limited data availability. This study applied artificial neural networks (ANNs) and empirical models to predict runoff in the Bhima River basin. Among the tested models, the ANN-5 model, which utilized rainfall and one-day delayed rainfall as inputs, demonstrated superior performance with minimal error and high efficiency. Statistical results for the ANN-5 model showed excellent outcomes during both training (R = 0.95, NSE = 0.89, RMSE = 17.39, MAE = 0.12, d = 0.97, MBE = 0.12) and testing (R = 0.94, NSE = 0.88, RMSE = 11.47, MAE = 0.03, d = 0.97, MBE = 0.03). Among empirical models, the Coutagine model was the most accurate, with R = 0.82, MBE = 74.36, NSE = 0.94, d = 0.82, KGE = 0.76, MAE = 70.01, MAPE = 20.6%, NRMSE = 0.22, RMSE = 87.4, and DRV = −9.2. In contrast, Khosla's formula (KF) significantly overestimated runoff. The close correlation between observed and ANN-predicted runoff data underscores the model's utility for decision-makers in inflow forecasting, water resource planning, management, and flood forecasting.","PeriodicalId":104096,"journal":{"name":"Water Practice & Technology","volume":"24 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141338930","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

Nitrogen recovery from reject water for improved sustainability of wastewater treatment 从废水中回收氮，提高污水处理的可持续性

Water Practice & Technology Pub Date : 2024-06-13 DOI: 10.2166/wpt.2024.156

Hakan Jönsson, A. Malovanyy, S. Tumlin

{"title":"Nitrogen recovery from reject water for improved sustainability of wastewater treatment","authors":"Hakan Jönsson, A. Malovanyy, S. Tumlin","doi":"10.2166/wpt.2024.156","DOIUrl":"https://doi.org/10.2166/wpt.2024.156","url":null,"abstract":"\u0000 \u0000 Flows of reactive nitrogen (Nr) and greenhouse gas emissions from society are exceeding planetary boundaries, posing a serious risk to the stability of living conditions on Earth. Wastewater contains the largest flows of Nr in urban society, so recycling Nr from wastewater treatment plants (WWTPs) could reduce the climate impact and the need for new Nr. The reject water from dewatering anaerobically digested sludge contains high concentrations of Nr and recovery of this Nr would decrease the load on biological nitrogen removal processes, and thus nitrous oxide emissions. Simultaneously, the need for external carbon sources and energy for aeration will decrease. In a case study at Rya WWTP in Gothenburg, Sweden, three Nr recovery technologies were investigated: (1) conventional ammonia stripping to ammonium sulphate; (2) thermal stripping to ammonium sulphate and (3) distillation of ammonia from reject water to ammonia water. All three technologies were found to decrease the climate impact compared with the removal processes currently used at Rya WWTP for the removal of Nr. Recovery by distillation to ammonia water had the lowest climate impact, while conventional stripping minimised the energy requirement.","PeriodicalId":104096,"journal":{"name":"Water Practice & Technology","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141348894","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

Water quality assessment of Loktak Lake, Manipur using Landsat 9 imagery 利用 Landsat 9 图像评估曼尼普尔 Loktak 湖的水质

Water Practice & Technology Pub Date : 2024-06-11 DOI: 10.2166/wpt.2024.154

Rajkumar Birendrakumar Singh, G. S. Yurembam, Deepak Jhajharia, B. C. Kusre

{"title":"Water quality assessment of Loktak Lake, Manipur using Landsat 9 imagery","authors":"Rajkumar Birendrakumar Singh, G. S. Yurembam, Deepak Jhajharia, B. C. Kusre","doi":"10.2166/wpt.2024.154","DOIUrl":"https://doi.org/10.2166/wpt.2024.154","url":null,"abstract":"\u0000 \u0000 The role of freshwater lakes in providing water resources and supporting ecosystems is essential. Monitoring water quality using remote sensing (RS) technologies is crucial for sustainable management practices. A study on Loktak Lake was done using RS algorithms to predict post-monsoon water quality. The multiplication band model (B1 × B6) demonstrated a moderate correlation with dissolved oxygen (DO) values (mg/l) with (coefficient of determination, R2 = 0.47, root mean square error, RMSE = 0.23, and standard error of estimation, SEE = 0.23). The band combination (B2/B4) was strongly correlated with electrical conductivity (EC) values (μs/cm) (R2 = 0.60, RMSE = 9.44, and SEE = 9.69). For total dissolved solids (TDS) (mg/l), with an R2 = 0.61, RMSE = 5.95, and SEE = 6.09, Band 2 demonstrated a strong correlation between field values and satellite imagery. The post-monsoon water quality map of the lake indicates lower concentrations of DO, EC, and TDS on the western side and elevated values on the eastern side. The research concluded that RS algorithms can be effectively used to predict water quality parameters in Loktak Lake, specifically DO, EC, and TDS. The findings suggest that effective pollution management is needed on the western side of the lake.","PeriodicalId":104096,"journal":{"name":"Water Practice & Technology","volume":"27 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141357235","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

Fenton-reactive groundwater circulation well for groundwater remediation of a toluene-contaminated site 用于甲苯污染场地地下水修复的芬顿反应地下水循环井

Water Practice & Technology Pub Date : 2024-06-10 DOI: 10.2166/wpt.2024.155

Biao Yu, Zhihui Qu, Liuhui Jiang, Haowei Yuan, Yan Su, Qian Liu

引用次数: 0

Developing a machine learning-based flood risk prediction model for the Indus Basin in Pakistan 为巴基斯坦印度河流域开发基于机器学习的洪水风险预测模型

Water Practice & Technology Pub Date : 2024-06-08 DOI: 10.2166/wpt.2024.151

Mehran Khan, A. Khan, Basir Ullah, Sunaid Khan

{"title":"Developing a machine learning-based flood risk prediction model for the Indus Basin in Pakistan","authors":"Mehran Khan, A. Khan, Basir Ullah, Sunaid Khan","doi":"10.2166/wpt.2024.151","DOIUrl":"https://doi.org/10.2166/wpt.2024.151","url":null,"abstract":"\u0000 \u0000 Pakistan is highly prone to devastating floods, as seen in the June 2010 and September 2022 disasters. The 2010 floods affected 20 million people, causing 1,985 fatalities. In 2022, approximately 33 million individuals were impacted, with multiple districts declared as ‘calamity struck’ by the National Disaster Management Authority (NDMA). Since June 14th, these floods have caused the loss of approximately 1,400 lives. Hence, the urgent necessity to develop an accurate and efficient flood risk prediction system for early warning purposes in Pakistan. This research aims to address this need by developing a predictive model using machine learning (ML) techniques such as k-nearest neighbors (KNN), support vector machine (SVM), Naive Bayes (NB), artificial neural network (ANN), and random forest (RF) for flood risk prediction in the Indus Basin of Pakistan. The performance of each model was evaluated based on accuracy, precision, recall, and F-measure. The findings revealed that SVM outperformed the other models, achieving an accuracy of 82.40%. Consequently, the results of this study can provide valuable insights for organizations to proactively mitigate frequent flood occurrences in Pakistan, aiding preventive actions.","PeriodicalId":104096,"journal":{"name":"Water Practice & Technology","volume":" 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369822","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

Numerical simulation of flow characteristics in nonprismatic compound channels 非棱柱复合渠道中流动特性的数值模拟

Water Practice & Technology Pub Date : 2024-06-08 DOI: 10.2166/wpt.2024.153

Vijay Kaushik, Munendra Kumar, Bandita Naik

{"title":"Numerical simulation of flow characteristics in nonprismatic compound channels","authors":"Vijay Kaushik, Munendra Kumar, Bandita Naik","doi":"10.2166/wpt.2024.153","DOIUrl":"https://doi.org/10.2166/wpt.2024.153","url":null,"abstract":"\u0000 \u0000 The assessment of the flow characteristics of river systems is a very intricate undertaking in the development of hydraulic models for the purposes of flood control and floodplain management. Therefore, it is essential to use simulation models in order to calibrate and verify the experimental results. In this study, the Hydrologic Engineering Centre's – River Analysis System (HEC-RAS) is used to calibrate and validate the distribution of velocity and shear stress for different converging compound channels. Two separate flow regimes were assessed for validation based on experimental data obtained from converging compound channels with angles of θ = 5°, 9°, and 12.38°. The projected values for two relative depths (β = 0.15 and 0.20) exhibit a similar pattern of variation as the empirical observations and are marginally lower than the recorded values. This suggests that the HEC-RAS model accurately estimates the velocity and shear stress values. The disparity between the simulated and experimental outcomes shows a discrepancy of less than 10%. Hence, the implications of our results suggest that while dealing with nonprismatic rivers, it is advisable to take into account lower values. The used methodology and the outcomes focused on problem-solving might potentially inform the development of flood control infrastructure for nonprismatic watercourses.","PeriodicalId":104096,"journal":{"name":"Water Practice & Technology","volume":" 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141370171","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

Wavelet-ANN hybrid model evaluation in seepage prediction in nonhomogeneous earth dams 非均质土坝渗流预测中的 Wavelet-ANN 混合模型评估

Water Practice & Technology Pub Date : 2024-06-08 DOI: 10.2166/wpt.2024.152

Bahador Fatehi-Nobarian, Sina Fard Moradinia

{"title":"Wavelet-ANN hybrid model evaluation in seepage prediction in nonhomogeneous earth dams","authors":"Bahador Fatehi-Nobarian, Sina Fard Moradinia","doi":"10.2166/wpt.2024.152","DOIUrl":"https://doi.org/10.2166/wpt.2024.152","url":null,"abstract":"\u0000 \u0000 In this study, novel methods such as wavelet–artificial neural network hybrid models and artificial neural network models were used to predict seepage from the Zonouz earth dam. The dataset consisted of 972 piezometric data points. Statistical fitting methods such as root mean squared error, determination coefficient, scatter plots, and data distribution diagrams were used to evaluate the results. The findings indicated that the wavelet–artificial neural network hybrid model was more accurate than the artificial neural network model. Specifically, during training, the wavelet–artificial neural network hybrid model had determination coefficients and root mean squared errors of 0.820, 0.680, 743.39, and 792.52, while the artificial neural network model had 0.700, 0.600, 426.39, and 131.45. Similarly, during validation, the wavelet–artificial neural network hybrid model had determination coefficients and root mean squared errors of 0.700, 0.600, 426.39, and 131.45, while the artificial neural network model had 0.823, 0.680, 743.39, and 792.52. Therefore, the wavelet–artificial neural network hybrid model can be proposed as a precise method for predicting seepage in earth dams and is more accurate than the artificial neural network model. This study highlights the importance of preventing dam failures and using advanced modeling techniques for better predictions and preventive measures.","PeriodicalId":104096,"journal":{"name":"Water Practice & Technology","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369651","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

Contribution of GIS and remote sensing in evaluating groundwater potential zoning in Mbandjock 地理信息系统和遥感对姆班乔克地下水潜力分区评估的贡献

Water Practice & Technology Pub Date : 2024-06-06 DOI: 10.2166/wpt.2024.149

Didier Bah, Njogi Bella Anne Rosine Eve, Boris Merlain Djousse Kenou, Rigobert Motchemien, Mathias Fru Fonteh

引用次数: 0

Comparison of dissolved air flotation and sedimentation for the treatment of two waters with low turbidity in Colombia 比较溶气气浮法和沉淀法处理哥伦比亚两种低浊度水体的方法

Water Practice & Technology Pub Date : 2024-06-06 DOI: 10.2166/wpt.2024.150

J. P. González-Galvis, Angela María Jaramillo Londoño, Ruben Dario Sepúlveda Amaya

引用次数: 0

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