Multivariate modeling of river water withdrawal using a hybrid evolutionary data-driven method
Water resources are one of the most important features of the environment to meet human needs. In the current research, morphological, quantitative and qualitative hydrological, and land use factors as well as the combined factor, which is the combination of effective variables of the aforementioned...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2022-01-01
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| Series: | Water Supply |
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| Online Access: | http://ws.iwaponline.com/content/22/1/957 |
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| author | Alireza Emadi Reza Sobhani Hossein Ahmadi Arezoo Boroomandnia Sarvin Zamanzad-Ghavidel Hazi Mohammad Azamathulla |
| author_facet | Alireza Emadi Reza Sobhani Hossein Ahmadi Arezoo Boroomandnia Sarvin Zamanzad-Ghavidel Hazi Mohammad Azamathulla |
| author_sort | Alireza Emadi |
| collection | DOAJ |
| description | Water resources are one of the most important features of the environment to meet human needs. In the current research, morphological, quantitative and qualitative hydrological, and land use factors as well as the combined factor, which is the combination of effective variables of the aforementioned factors, have been used to estimate River Water Withdrawal (RWW) for agricultural uses. Lavasanat and Qazvin are selected as study areas, located in the Namak Lake basin in Iran, with Bsk and Csa climate categories, respectively. Estimation of RWW is performed using single and Wavelet–hybrid (W-hybrid) data-driven methods, including Artificial Neural Networks (ANNs), Wavelet–ANN (WANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), Wavelet–ANFIS (WANFIS), Gene Expression Programming (GEP), and Wavelet–GEP (WGEP). Due to the evaluation criteria, the performance of the WGEP model is the best among the others for estimating RWW variables in both study areas. Considering the W-hybrid models with data de-noising for estimating RWW in the Lavasanat and Qazvin study areas, the obtained values of RMSE for WGEP11 to WGEP15 and WGEP21 to WGEP25 equal 67.268, 54.659, 80.871, 50.796, 15.676 and 105.532, 96.615, 105.018, 160.961, 44.332, respectively. The results indicate that WGEP and ANN are the best and poorest models in both study areas without regarding climate condition effects. Also, a combined factor which includes River Width (RW), minimum flow rate (QMin), average flow rate (QMean), Electrical Conductivity (EC), and Cultivated Area (CA) variables is introduced as the best factor to estimate RWW variables compared with the other factors in both the Bsk and Csa climate categories. On the other hand, qualitative hydrological and land use factors were the weakest ones to estimate RWW variables in the Bsk and Csa climate categories, respectively. Therefore, the current study explores how the mathematical relations for estimating RWW have a significant effect on water resources management and planning by policymakers in the future. HIGHLIGHTS
River Water Withdrawal (RWW) for agricultural purposes was estimated using data-driven methods.;
The impact of climatic condition, river morphology, quantitative and qualitative hydrological characteristics, and land use features on RWW estimation was assessed.;
De-noising the data and developing the combined factor could improve the model's performance.; |
| first_indexed | 2024-12-13T10:04:28Z |
| format | Article |
| id | doaj.art-599f94d5d2a24e8ca8c518e1953a880c |
| institution | Directory Open Access Journal |
| issn | 1606-9749 1607-0798 |
| language | English |
| last_indexed | 2024-12-13T10:04:28Z |
| publishDate | 2022-01-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Water Supply |
| spelling | doaj.art-599f94d5d2a24e8ca8c518e1953a880c2022-12-21T23:51:34ZengIWA PublishingWater Supply1606-97491607-07982022-01-0122195798010.2166/ws.2021.224224Multivariate modeling of river water withdrawal using a hybrid evolutionary data-driven methodAlireza Emadi0Reza Sobhani1Hossein Ahmadi2Arezoo Boroomandnia3Sarvin Zamanzad-Ghavidel4Hazi Mohammad Azamathulla5 Department of Water Engineering, Sari Agricultural Science and Natural Resources University, Sari, Iran Department of Water Engineering, Sari Agricultural Science and Natural Resources University, Sari, Iran Department of Irrigation & Reclamation Engineering, Faculty of Agriculture Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Alborz, Iran Department of Irrigation & Reclamation Engineering, Faculty of Agriculture Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Alborz, Iran Department of Irrigation & Reclamation Engineering, Faculty of Agriculture Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Alborz, Iran Department of Civil and Environmental Engineering, University of the West Indies, St. Augustine, Trinidad and Tobago Water resources are one of the most important features of the environment to meet human needs. In the current research, morphological, quantitative and qualitative hydrological, and land use factors as well as the combined factor, which is the combination of effective variables of the aforementioned factors, have been used to estimate River Water Withdrawal (RWW) for agricultural uses. Lavasanat and Qazvin are selected as study areas, located in the Namak Lake basin in Iran, with Bsk and Csa climate categories, respectively. Estimation of RWW is performed using single and Wavelet–hybrid (W-hybrid) data-driven methods, including Artificial Neural Networks (ANNs), Wavelet–ANN (WANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), Wavelet–ANFIS (WANFIS), Gene Expression Programming (GEP), and Wavelet–GEP (WGEP). Due to the evaluation criteria, the performance of the WGEP model is the best among the others for estimating RWW variables in both study areas. Considering the W-hybrid models with data de-noising for estimating RWW in the Lavasanat and Qazvin study areas, the obtained values of RMSE for WGEP11 to WGEP15 and WGEP21 to WGEP25 equal 67.268, 54.659, 80.871, 50.796, 15.676 and 105.532, 96.615, 105.018, 160.961, 44.332, respectively. The results indicate that WGEP and ANN are the best and poorest models in both study areas without regarding climate condition effects. Also, a combined factor which includes River Width (RW), minimum flow rate (QMin), average flow rate (QMean), Electrical Conductivity (EC), and Cultivated Area (CA) variables is introduced as the best factor to estimate RWW variables compared with the other factors in both the Bsk and Csa climate categories. On the other hand, qualitative hydrological and land use factors were the weakest ones to estimate RWW variables in the Bsk and Csa climate categories, respectively. Therefore, the current study explores how the mathematical relations for estimating RWW have a significant effect on water resources management and planning by policymakers in the future. HIGHLIGHTS River Water Withdrawal (RWW) for agricultural purposes was estimated using data-driven methods.; The impact of climatic condition, river morphology, quantitative and qualitative hydrological characteristics, and land use features on RWW estimation was assessed.; De-noising the data and developing the combined factor could improve the model's performance.;http://ws.iwaponline.com/content/22/1/957data-driven methodsmodelingwater withdrawalwavelet transform |
| spellingShingle | Alireza Emadi Reza Sobhani Hossein Ahmadi Arezoo Boroomandnia Sarvin Zamanzad-Ghavidel Hazi Mohammad Azamathulla Multivariate modeling of river water withdrawal using a hybrid evolutionary data-driven method Water Supply data-driven methods modeling water withdrawal wavelet transform |
| title | Multivariate modeling of river water withdrawal using a hybrid evolutionary data-driven method |
| title_full | Multivariate modeling of river water withdrawal using a hybrid evolutionary data-driven method |
| title_fullStr | Multivariate modeling of river water withdrawal using a hybrid evolutionary data-driven method |
| title_full_unstemmed | Multivariate modeling of river water withdrawal using a hybrid evolutionary data-driven method |
| title_short | Multivariate modeling of river water withdrawal using a hybrid evolutionary data-driven method |
| title_sort | multivariate modeling of river water withdrawal using a hybrid evolutionary data driven method |
| topic | data-driven methods modeling water withdrawal wavelet transform |
| url | http://ws.iwaponline.com/content/22/1/957 |
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