This paper takes the intelligent water level recognition instrument of Qingming Shanghe Park in Kaifeng as the experimental object, introduces the algorithm of strong edge and sparse constraint into the intelligent water level recognition instrument, and compares the recognition effect of the intelligent water level recognition instrument before and after the introduction of strong edge and sparse constraint algorithms. The results showed that the clarity value was approximately 10% higher, and the recognition speed was also significantly improved. The improvement of recognition speed can effectively promote the work efficiency of the whole method. Strong edges and sparse constraints can effectively improve the accuracy of water level identification, provide scientific and effective data and information for subsequent water resource management, and meet the needs of water resource managers to effectively grasp the law of water level. This can provide technical support for identification methods in other fields, and the ultimate goal is to promote the protection and management of water resources and reduce the harm of natural disasters on people.
{"title":"Water level recognition based on strong edge and sparse constraints","authors":"Guoheng Ren, Wei Wang, Hanyu Wei, Xiaofeng Li","doi":"10.2166/ws.2023.221","DOIUrl":"https://doi.org/10.2166/ws.2023.221","url":null,"abstract":"\u0000 \u0000 This paper takes the intelligent water level recognition instrument of Qingming Shanghe Park in Kaifeng as the experimental object, introduces the algorithm of strong edge and sparse constraint into the intelligent water level recognition instrument, and compares the recognition effect of the intelligent water level recognition instrument before and after the introduction of strong edge and sparse constraint algorithms. The results showed that the clarity value was approximately 10% higher, and the recognition speed was also significantly improved. The improvement of recognition speed can effectively promote the work efficiency of the whole method. Strong edges and sparse constraints can effectively improve the accuracy of water level identification, provide scientific and effective data and information for subsequent water resource management, and meet the needs of water resource managers to effectively grasp the law of water level. This can provide technical support for identification methods in other fields, and the ultimate goal is to promote the protection and management of water resources and reduce the harm of natural disasters on people.","PeriodicalId":17553,"journal":{"name":"Journal of Water Supply Research and Technology-aqua","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81388445","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}
In this study, two artificial intelligence techniques: (1) artificial neural networks (ANNs) using different algorithms such as Lavenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) and (2) Adaptive Neuro-Fuzzy Inference System (ANFIS) are used to predict velocity and pressure for Gadhra (DMA-5) real water distribution network (WDN), East Singhbhum district of Jharkhand, India. In case 1, flow rate and diameter are used as independent variables to predict velocity. In case 2, elevation and demand are used as independent variables to predict pressure. 80% of the data are used to train, test, and validate the ANN and ANFIS prediction models, while 20% of the data are used to evaluate data-driven models. Sensitivity analysis is performed in ANN-LM to understand the relationship between the independent and dependent variables. The performance indices of RMSE, MAE, and R2 are evaluated for ANN and ANFIS for different combinations. The ANN-LM, with 2-16-1 architecture, is found as a superior to predict velocity and ANN-LM with architecture 2-17-1 is found as a superior to predict pressure. ANN-LM had the best prediction in estimating velocity (RMSE = 0.0189, MAE = 0.0122, R2 = 0.9568) and pressure (RMSE = 0.3244, MAE = 0.2176, R2 = 0.9773).
本研究采用两种人工智能技术:(1)采用Lavenberg-Marquardt (LM)、贝叶斯正则化(BR)和缩放共轭梯度(SCG)等不同算法的人工神经网络(ann)和(2)自适应神经模糊推理系统(ANFIS)对印度贾坎德邦东Singhbhum地区Gadhra (DMA-5)实际配水网络(WDN)的流速和压力进行预测。在情形1中，流速和直径作为独立变量来预测速度。在情形2中，使用标高和需求作为独立变量来预测压力。80%的数据用于训练、测试和验证ANN和ANFIS预测模型，而20%的数据用于评估数据驱动模型。在ANN-LM中进行敏感性分析，以了解自变量和因变量之间的关系。对ANN和ANFIS在不同组合下的RMSE、MAE和R2性能指标进行了评价。结果表明，2-16-1结构的ANN-LM在速度预测上优于2-17-1结构的ANN-LM在压力预测上优于2-17-1结构的ANN-LM。ANN-LM对速度(RMSE = 0.0189, MAE = 0.0122, R2 = 0.9568)和压力(RMSE = 0.3244, MAE = 0.2176, R2 = 0.9773)的预测效果最好。
{"title":"Performance evaluation of ANN and ANFIS models for estimating velocity and pressure in water distribution networks","authors":"A. Rashid, Sangeeta Kumari","doi":"10.2166/ws.2023.224","DOIUrl":"https://doi.org/10.2166/ws.2023.224","url":null,"abstract":"\u0000 \u0000 In this study, two artificial intelligence techniques: (1) artificial neural networks (ANNs) using different algorithms such as Lavenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) and (2) Adaptive Neuro-Fuzzy Inference System (ANFIS) are used to predict velocity and pressure for Gadhra (DMA-5) real water distribution network (WDN), East Singhbhum district of Jharkhand, India. In case 1, flow rate and diameter are used as independent variables to predict velocity. In case 2, elevation and demand are used as independent variables to predict pressure. 80% of the data are used to train, test, and validate the ANN and ANFIS prediction models, while 20% of the data are used to evaluate data-driven models. Sensitivity analysis is performed in ANN-LM to understand the relationship between the independent and dependent variables. The performance indices of RMSE, MAE, and R2 are evaluated for ANN and ANFIS for different combinations. The ANN-LM, with 2-16-1 architecture, is found as a superior to predict velocity and ANN-LM with architecture 2-17-1 is found as a superior to predict pressure. ANN-LM had the best prediction in estimating velocity (RMSE = 0.0189, MAE = 0.0122, R2 = 0.9568) and pressure (RMSE = 0.3244, MAE = 0.2176, R2 = 0.9773).","PeriodicalId":17553,"journal":{"name":"Journal of Water Supply Research and Technology-aqua","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87429808","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}
Eloiza Laisla Lino Tochio, Bruno Cézar do Nascimento, S. Lautenschlager
Coagulation is an important water treatment step in a water treatment plant (WTP). Jar tests are performed to determine the required dose of coagulant; however, these tests are slow to be performed and do not give a response in real-time to changes in raw water quality that changes abruptly during the day. To overcome this limitation, this research developed artificial neural network (ANN) models, using full-scale WTP data that served to calibrate the model and then predict the coagulant dosage, considering raw water as data input, in compliance with the treated water quality parameters. The best model was able to predict the coagulant dosage with a mean squared error of 0.016 and a correlation coefficient equal to 0.872. These results corroborate to promote coagulant dosage automation in WTPs, making it clear that ANN models allow a faster response in dosage definition and reduce the need for human interaction in the process.
{"title":"Coagulant dosage prediction in the water treatment process","authors":"Eloiza Laisla Lino Tochio, Bruno Cézar do Nascimento, S. Lautenschlager","doi":"10.2166/ws.2023.219","DOIUrl":"https://doi.org/10.2166/ws.2023.219","url":null,"abstract":"\u0000 Coagulation is an important water treatment step in a water treatment plant (WTP). Jar tests are performed to determine the required dose of coagulant; however, these tests are slow to be performed and do not give a response in real-time to changes in raw water quality that changes abruptly during the day. To overcome this limitation, this research developed artificial neural network (ANN) models, using full-scale WTP data that served to calibrate the model and then predict the coagulant dosage, considering raw water as data input, in compliance with the treated water quality parameters. The best model was able to predict the coagulant dosage with a mean squared error of 0.016 and a correlation coefficient equal to 0.872. These results corroborate to promote coagulant dosage automation in WTPs, making it clear that ANN models allow a faster response in dosage definition and reduce the need for human interaction in the process.","PeriodicalId":17553,"journal":{"name":"Journal of Water Supply Research and Technology-aqua","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89108332","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}
Xiangqiu Zhang, Xuewei Wu, Yongqin Yuan, Z. Long, Tingchao Yu
This research article presents a data-driven approach for detecting bursts in water distribution networks (WDNs). The framework uses spatiotemporal information from monitoring pressure and unsupervised learning model. This approach employs three stages: (1) benchmark dataset acquisition, (2) spatiotemporal information analysis, and (3) burst detection model construction. First, the benchmark datasets were the normal dataset initially obtained by the clustering algorithm. Second, spatiotemporal information features are extracted from multimoment time windows from multiple sensors, including the distance and shape features. Third, burst detection was performed based on the isolation forest technique. A WDN is used to evaluate the performance of the method. Results show that the method can effectively detect the burst.
{"title":"Burst detection based on multi-time monitoring data from multiple pressure sensors in district metering areas","authors":"Xiangqiu Zhang, Xuewei Wu, Yongqin Yuan, Z. Long, Tingchao Yu","doi":"10.2166/ws.2023.220","DOIUrl":"https://doi.org/10.2166/ws.2023.220","url":null,"abstract":"\u0000 \u0000 This research article presents a data-driven approach for detecting bursts in water distribution networks (WDNs). The framework uses spatiotemporal information from monitoring pressure and unsupervised learning model. This approach employs three stages: (1) benchmark dataset acquisition, (2) spatiotemporal information analysis, and (3) burst detection model construction. First, the benchmark datasets were the normal dataset initially obtained by the clustering algorithm. Second, spatiotemporal information features are extracted from multimoment time windows from multiple sensors, including the distance and shape features. Third, burst detection was performed based on the isolation forest technique. A WDN is used to evaluate the performance of the method. Results show that the method can effectively detect the burst.","PeriodicalId":17553,"journal":{"name":"Journal of Water Supply Research and Technology-aqua","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76912363","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}
In arid and semi-arid areas where available water resources are very limited, the application of unconventional sources of water like the fog is of paramount importance. In this paper, the feasibility of using a standard fog collector (SFC) to collect fog water for complementary irrigation of rainfed wheat in the Abi-beyglu area was investigated. For this purpose, collected water volume was measured on a daily basis during fog time in 2021. The water demand of the winter wheat was estimated by the FAO Penman–Monteith equation under dry and normal conditions. Then, the contribution of the collected water to supply the water demand of the wheat and the resultant increase in the yield under two different scenarios, namely complementary irrigation with 30 and 60 mm of collected water, was estimated using the AquaCrop model. Results showed that it is feasible to obtain an average water production of 3.6 L/m2/day over the studied period. Upon irrigation with 30 and 60 mm of collected water under dry and normal conditions, 26 and 34% of the water deficiency for wheat farming was supplied, leading to increased crop yields by 0.6 and 1.7 ton/ha, respectively.
{"title":"Fog water harvesting potential and its use in supplementary irrigation of rainfed crops (winter wheat) in Abi-beyglu, Ardabil (Iran)","authors":"A. Kanooni, Mohammad Reza Kohan","doi":"10.2166/ws.2023.217","DOIUrl":"https://doi.org/10.2166/ws.2023.217","url":null,"abstract":"\u0000 \u0000 In arid and semi-arid areas where available water resources are very limited, the application of unconventional sources of water like the fog is of paramount importance. In this paper, the feasibility of using a standard fog collector (SFC) to collect fog water for complementary irrigation of rainfed wheat in the Abi-beyglu area was investigated. For this purpose, collected water volume was measured on a daily basis during fog time in 2021. The water demand of the winter wheat was estimated by the FAO Penman–Monteith equation under dry and normal conditions. Then, the contribution of the collected water to supply the water demand of the wheat and the resultant increase in the yield under two different scenarios, namely complementary irrigation with 30 and 60 mm of collected water, was estimated using the AquaCrop model. Results showed that it is feasible to obtain an average water production of 3.6 L/m2/day over the studied period. Upon irrigation with 30 and 60 mm of collected water under dry and normal conditions, 26 and 34% of the water deficiency for wheat farming was supplied, leading to increased crop yields by 0.6 and 1.7 ton/ha, respectively.","PeriodicalId":17553,"journal":{"name":"Journal of Water Supply Research and Technology-aqua","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89895573","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}
Free overfalls are hydraulic structures used in flood control, water supply, irrigation, and flow measurements. The hydraulic systems of free overfall depend on rectangular end shape. The studies that dealt with triangular crest are few and almost non-existent. In this study, a triangular end-shape design uses multiple linear regression (MLR) and group method of data handling (GMDH) methods for four models with six sub-models. Then, 24 scenarios were chosen and compared. The discharge coefficient (Cd) of a free overfall with a triangular terminal was predicted using experimental data. The triangular end edge shape increased crest length, the discharge coefficient, and discharge passing over free overfall. To this goal, 180 triangular free fall tests were performed. Data were collected for two triangular free overfalls with an opposite flow direction with three angles 600, 750, and 900. Results of Cd acquired using the two ways discussed above show that the algorithm GMDH outperforms the other method. Values for the GMDH approach mod46 testing variables: RMSE, MARE, SI, R2, and NSE are 6.08E-17, 2.65E-17, 6.00E-17, 100.00%, and 1.00, respectively, while these values for MLR are 0.06332, 0.05970, 0.06624, 15.431%, and −3.0419, respectively. The GMDH technique shows the best results concerning MLR and then chooses the best four scenarios from 24 with a Cd percentage error not exceeding ±2%.
{"title":"Estimating discharge coefficient of triangular free overfall using the GMDH technique","authors":"A. Mohammed, A. Sharifi","doi":"10.2166/ws.2023.218","DOIUrl":"https://doi.org/10.2166/ws.2023.218","url":null,"abstract":"\u0000 Free overfalls are hydraulic structures used in flood control, water supply, irrigation, and flow measurements. The hydraulic systems of free overfall depend on rectangular end shape. The studies that dealt with triangular crest are few and almost non-existent. In this study, a triangular end-shape design uses multiple linear regression (MLR) and group method of data handling (GMDH) methods for four models with six sub-models. Then, 24 scenarios were chosen and compared. The discharge coefficient (Cd) of a free overfall with a triangular terminal was predicted using experimental data. The triangular end edge shape increased crest length, the discharge coefficient, and discharge passing over free overfall. To this goal, 180 triangular free fall tests were performed. Data were collected for two triangular free overfalls with an opposite flow direction with three angles 600, 750, and 900. Results of Cd acquired using the two ways discussed above show that the algorithm GMDH outperforms the other method. Values for the GMDH approach mod46 testing variables: RMSE, MARE, SI, R2, and NSE are 6.08E-17, 2.65E-17, 6.00E-17, 100.00%, and 1.00, respectively, while these values for MLR are 0.06332, 0.05970, 0.06624, 15.431%, and −3.0419, respectively. The GMDH technique shows the best results concerning MLR and then chooses the best four scenarios from 24 with a Cd percentage error not exceeding ±2%.","PeriodicalId":17553,"journal":{"name":"Journal of Water Supply Research and Technology-aqua","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74766205","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}
Surface water bodies are vulnerable to cyanobacteria overgrowth, primarily owing to nutrient enrichment, rising temperatures, and recurrent droughts. Regular cyanobacteria monitoring in water systems is crucial to prevent and manage health risks associated with toxin exposure. Surface water samples were collected from the Jundiai River in Sao Paulo State, Brazil for 3 years (2018–2022) to study the seasonal changes and species diversity of cyanobacteria. The study also aimed to understand the relationship between cyanobacteria abundance, climate, water quality, and hydrological parameters. Data analyses revealed a pattern of significantly elevated cyanobacterial cell counts during the dry season (DS), accompanied by an increase in the cyanobacterial species. The identified species poses a threat to water safety owing to the potential production of toxins, as well as causing unpleasant taste and odor. The DS is marked by higher nutrient concentrations and lower water flow. Phosphorus levels remain high, allowing cyanobacteria to grow without being limited by nutrients. In future scenarios, the primary concern for the Jundiai River is not temperature rise but droughts that create a stable environment for cyanobacteria proliferation. This research provides valuable data for river water users and contributes to a broader understanding of the global cyanobacterial dispersion.
{"title":"Seasonal dynamics and diversity of cyanobacteria in a eutrophied Urban River in Brazil","authors":"A. Mânica, Ricardo de Lima Isaac","doi":"10.2166/ws.2023.216","DOIUrl":"https://doi.org/10.2166/ws.2023.216","url":null,"abstract":"\u0000 \u0000 Surface water bodies are vulnerable to cyanobacteria overgrowth, primarily owing to nutrient enrichment, rising temperatures, and recurrent droughts. Regular cyanobacteria monitoring in water systems is crucial to prevent and manage health risks associated with toxin exposure. Surface water samples were collected from the Jundiai River in Sao Paulo State, Brazil for 3 years (2018–2022) to study the seasonal changes and species diversity of cyanobacteria. The study also aimed to understand the relationship between cyanobacteria abundance, climate, water quality, and hydrological parameters. Data analyses revealed a pattern of significantly elevated cyanobacterial cell counts during the dry season (DS), accompanied by an increase in the cyanobacterial species. The identified species poses a threat to water safety owing to the potential production of toxins, as well as causing unpleasant taste and odor. The DS is marked by higher nutrient concentrations and lower water flow. Phosphorus levels remain high, allowing cyanobacteria to grow without being limited by nutrients. In future scenarios, the primary concern for the Jundiai River is not temperature rise but droughts that create a stable environment for cyanobacteria proliferation. This research provides valuable data for river water users and contributes to a broader understanding of the global cyanobacterial dispersion.","PeriodicalId":17553,"journal":{"name":"Journal of Water Supply Research and Technology-aqua","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88505214","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}
Accurate prediction of monthly precipitation is crucial for effective regional water resources management and utilization. However, precipitation series are influenced by multiple factors, exhibiting significant ambiguity, chance, and uncertainty. In this research, we propose a combined model that integrates adaptive noise-complete ensemble empirical mode decomposition (CEEMDAN), variational modal decomposition method (VMD), and bidirectional long- and short-term memory (BILSTM) to enhance precipitation prediction. We apply this model to forecast precipitation in Fuzhou City and compare its performance with existing models, including CEEMD–long and short-term memory (LSTM), CEEMD–BILSTM, and CEEMDAN–BILSTM. Our findings demonstrate that the combined CEEMDAN–VMD–BILSTM quadratic decomposition model yields more accurate predictions and captures the real variation in precipitation series with greater fidelity. The model achieves an average relative error of 1.69%, at a lower level, and an average absolute error of 1.32 m, with a Nash–Sutcliffe efficiency coefficient of 0.92. Overall, the proposed quadratic decomposition model exhibits excellent applicability, stability, and superior predictive capabilities in monthly precipitation forecasting.
月降水量的准确预报对区域水资源的有效管理和利用至关重要。降水序列受多种因素的影响，具有明显的模糊性、偶然性和不确定性。本研究提出了一种结合自适应噪声完全系综经验模态分解(CEEMDAN)、变分模态分解(VMD)和双向长短期记忆(BILSTM)的组合模型来增强降水预测。将该模型应用于福州地区的降水预报，并与现有模型(ceemd -长短期记忆(LSTM)、CEEMD-BILSTM和CEEMDAN-BILSTM)进行了比较。我们的研究结果表明，CEEMDAN-VMD-BILSTM组合二次分解模型可以更准确地预测降水序列的真实变化，并且具有更高的保真度。模型在较低水平上的平均相对误差为1.69%，平均绝对误差为1.32 m, Nash-Sutcliffe效率系数为0.92。总体而言，本文提出的二次分解模型在月降水预报中具有良好的适用性、稳定性和较强的预测能力。
{"title":"Precipitation prediction based on CEEMDAN–VMD–BILSTM combined quadratic decomposition model","authors":"Xianqi Zhang, Jingwen Shi, Haiyang Chen, Yimeng Xiao, Minghui Zhang","doi":"10.2166/ws.2023.212","DOIUrl":"https://doi.org/10.2166/ws.2023.212","url":null,"abstract":"\u0000 \u0000 Accurate prediction of monthly precipitation is crucial for effective regional water resources management and utilization. However, precipitation series are influenced by multiple factors, exhibiting significant ambiguity, chance, and uncertainty. In this research, we propose a combined model that integrates adaptive noise-complete ensemble empirical mode decomposition (CEEMDAN), variational modal decomposition method (VMD), and bidirectional long- and short-term memory (BILSTM) to enhance precipitation prediction. We apply this model to forecast precipitation in Fuzhou City and compare its performance with existing models, including CEEMD–long and short-term memory (LSTM), CEEMD–BILSTM, and CEEMDAN–BILSTM. Our findings demonstrate that the combined CEEMDAN–VMD–BILSTM quadratic decomposition model yields more accurate predictions and captures the real variation in precipitation series with greater fidelity. The model achieves an average relative error of 1.69%, at a lower level, and an average absolute error of 1.32 m, with a Nash–Sutcliffe efficiency coefficient of 0.92. Overall, the proposed quadratic decomposition model exhibits excellent applicability, stability, and superior predictive capabilities in monthly precipitation forecasting.","PeriodicalId":17553,"journal":{"name":"Journal of Water Supply Research and Technology-aqua","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75141036","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}
Operating a reservoir during flooding is a complex problem in which optimum decision-making is a difficult task. The present study demonstrates a solution for the operation of flooding problem in a multiple-purpose reservoir. A reservoir on River Narmada in central India is chosen as the case study. The multiple objective problems comprised maximization of hydropower releases, minimizing spills, and achieving stipulated target storage at the end of the operation period. The chosen optimization models are the Differential Evaluation Algorithm (DEA) and its variants: the Enhanced Differential Evolution Algorithm (EDEA) and the Modified Enhanced Differential Algorithm (MEDEA). The EDEA model is modified in the present study to MEDEA. The results of all three models applied to the same case study are compared on convergence to an optimal solution. All three algorithms were tested on two of the popular benchmark functions that are Ackley and Sphere. The results of both applications demonstrated that MEDEA proved to be the best in terms of converging to the optimal solution, exhibiting better stability, and quality of final results.
{"title":"Application of modified enhanced differential evolution algorithms for reservoir operation during floods: a case study","authors":"L. Sinha, S. Narulkar","doi":"10.2166/ws.2023.213","DOIUrl":"https://doi.org/10.2166/ws.2023.213","url":null,"abstract":"\u0000 \u0000 Operating a reservoir during flooding is a complex problem in which optimum decision-making is a difficult task. The present study demonstrates a solution for the operation of flooding problem in a multiple-purpose reservoir. A reservoir on River Narmada in central India is chosen as the case study. The multiple objective problems comprised maximization of hydropower releases, minimizing spills, and achieving stipulated target storage at the end of the operation period. The chosen optimization models are the Differential Evaluation Algorithm (DEA) and its variants: the Enhanced Differential Evolution Algorithm (EDEA) and the Modified Enhanced Differential Algorithm (MEDEA). The EDEA model is modified in the present study to MEDEA. The results of all three models applied to the same case study are compared on convergence to an optimal solution. All three algorithms were tested on two of the popular benchmark functions that are Ackley and Sphere. The results of both applications demonstrated that MEDEA proved to be the best in terms of converging to the optimal solution, exhibiting better stability, and quality of final results.","PeriodicalId":17553,"journal":{"name":"Journal of Water Supply Research and Technology-aqua","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82011236","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}
Wetlands, as a special ecological environment, are not only important biodiversity conservation areas but also one of the important agricultural resources. Agriculture plays an irreplaceable role in human society. It is directly related to human survival and development, and is also a part of people's environmental awareness and cultural inheritance. Based on the principles of sustainable development and strengthening environmental protection, people should pay more attention to the development and improvement of agriculture. However, with the advancement of urbanization, the area of wetlands continues to decrease, causing damage to ecosystems and posing a threat to some agricultural production. This article combined the transfer matrix of agricultural wetland utilization, landscape change rate, and landscape pattern index, used RS (Remote Sensing) and GIS (Geographic Information System) technologies to analyze the dynamic changes in agricultural wetland utilization and landscape of Honghu Lake in the Four Lakes region, and explored its changing factors. The results indicated that the construction land area showed an increasing trend in 2016, 2019, and 2022, while the wetland area of rice fields showed a first decreasing and then increasing trend.
湿地作为一种特殊的生态环境，不仅是重要的生物多样性保护区，也是重要的农业资源之一。农业在人类社会中起着不可替代的作用。它直接关系到人类的生存和发展，也是人们环保意识和文化传承的一部分。基于可持续发展和加强环境保护的原则，人们应该更加关注农业的发展和改善。然而，随着城市化进程的推进，湿地面积不断减少，对生态系统造成破坏，对部分农业生产构成威胁。本文结合农业湿地利用转移矩阵、景观变化率和景观格局指数，利用RS (Remote Sensing)和GIS (Geographic Information System)技术，分析了四湖地区洪湖农业湿地利用与景观的动态变化，并探讨了其变化因素。结果表明:2016年、2019年和2022年建设用地面积呈增加趋势，稻田湿地面积呈先减少后增加趋势;
{"title":"Agricultural wetland utilization based on land cover restoration and water–ecosystem nexus","authors":"Jing Li, Weiwei Liu, Ying Zhang","doi":"10.2166/ws.2023.215","DOIUrl":"https://doi.org/10.2166/ws.2023.215","url":null,"abstract":"\u0000 \u0000 Wetlands, as a special ecological environment, are not only important biodiversity conservation areas but also one of the important agricultural resources. Agriculture plays an irreplaceable role in human society. It is directly related to human survival and development, and is also a part of people's environmental awareness and cultural inheritance. Based on the principles of sustainable development and strengthening environmental protection, people should pay more attention to the development and improvement of agriculture. However, with the advancement of urbanization, the area of wetlands continues to decrease, causing damage to ecosystems and posing a threat to some agricultural production. This article combined the transfer matrix of agricultural wetland utilization, landscape change rate, and landscape pattern index, used RS (Remote Sensing) and GIS (Geographic Information System) technologies to analyze the dynamic changes in agricultural wetland utilization and landscape of Honghu Lake in the Four Lakes region, and explored its changing factors. The results indicated that the construction land area showed an increasing trend in 2016, 2019, and 2022, while the wetland area of rice fields showed a first decreasing and then increasing trend.","PeriodicalId":17553,"journal":{"name":"Journal of Water Supply Research and Technology-aqua","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80705013","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}
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