Using Artificial Intelligence to Identify Suitable Artificial Groundwater Recharge Areas for the Iranshahr Basin
A water supply is vital for preserving usual human living standards, industrial development, and agricultural growth. Scarce water supplies and unplanned urbanization are the primary impediments to results in dry environments. Locating suitable sites for artificial groundwater recharge (AGR) could b...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/6/1182 |
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| author | Mojtaba Zaresefat Reza Derakhshani Vahid Nikpeyman Amin GhasemiNejad Amir Raoof |
| author_facet | Mojtaba Zaresefat Reza Derakhshani Vahid Nikpeyman Amin GhasemiNejad Amir Raoof |
| author_sort | Mojtaba Zaresefat |
| collection | DOAJ |
| description | A water supply is vital for preserving usual human living standards, industrial development, and agricultural growth. Scarce water supplies and unplanned urbanization are the primary impediments to results in dry environments. Locating suitable sites for artificial groundwater recharge (AGR) could be a strategic priority for countries to recharge groundwater. Recent advances in machine learning (ML) techniques provide valuable tools for producing an AGR site suitability map (AGRSSM). This research developed an ML algorithm to identify the most appropriate location for AGR in Iranshahr, one of the major districts in the East of Iran characterized by severe drought and excessive groundwater consumption. The area’s undue reliance on groundwater resources has resulted in aquifer depletion and socioeconomic problems. Nine digitized and georeferenced data layers have been considered for preparing the AGRSSM, including precipitation, slope, geology, unsaturated zone thickness, land use, distance from the main rivers, precipitation, water quality, and transmissivity of soil. The developed AGRSSM was trained and validated using 1000 randomly selected points across the study area with an accuracy of 97%. By comparing the results of the proposed sites with those of other methods, it was discovered that the artificial intelligence method could accurately determine artificial recharge sites. In summary, this study uses a novel approach to identify optimal AGR sites using machine learning algorithms. Our findings have practical implications for policymakers and water resource managers looking to address the problem of groundwater depletion in Iranshahr and other regions facing similar challenges. Future research in this area could explore the applicability of our approach to other regions and examine the potential economic benefits of using AGR to recharge groundwater. |
| first_indexed | 2024-03-11T05:45:16Z |
| format | Article |
| id | doaj.art-ed8ce8e6330b499ca823099ed8ecae1b |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-11T05:45:16Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-ed8ce8e6330b499ca823099ed8ecae1b2023-11-17T14:27:38ZengMDPI AGWater2073-44412023-03-01156118210.3390/w15061182Using Artificial Intelligence to Identify Suitable Artificial Groundwater Recharge Areas for the Iranshahr BasinMojtaba Zaresefat0Reza Derakhshani1Vahid Nikpeyman2Amin GhasemiNejad3Amir Raoof4Copernicus Institute of Sustainable Development, Utrecht University, 3584 CB Utrecht, The NetherlandsDepartment of Geology, Shahid Bahonar University of Kerman, Kerman 76169-13439, IranDepartment of Earth Sciences, Utrecht University, 3584 CB Utrecht, The NetherlandsDepartment of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman 76169-13439, IranDepartment of Earth Sciences, Utrecht University, 3584 CB Utrecht, The NetherlandsA water supply is vital for preserving usual human living standards, industrial development, and agricultural growth. Scarce water supplies and unplanned urbanization are the primary impediments to results in dry environments. Locating suitable sites for artificial groundwater recharge (AGR) could be a strategic priority for countries to recharge groundwater. Recent advances in machine learning (ML) techniques provide valuable tools for producing an AGR site suitability map (AGRSSM). This research developed an ML algorithm to identify the most appropriate location for AGR in Iranshahr, one of the major districts in the East of Iran characterized by severe drought and excessive groundwater consumption. The area’s undue reliance on groundwater resources has resulted in aquifer depletion and socioeconomic problems. Nine digitized and georeferenced data layers have been considered for preparing the AGRSSM, including precipitation, slope, geology, unsaturated zone thickness, land use, distance from the main rivers, precipitation, water quality, and transmissivity of soil. The developed AGRSSM was trained and validated using 1000 randomly selected points across the study area with an accuracy of 97%. By comparing the results of the proposed sites with those of other methods, it was discovered that the artificial intelligence method could accurately determine artificial recharge sites. In summary, this study uses a novel approach to identify optimal AGR sites using machine learning algorithms. Our findings have practical implications for policymakers and water resource managers looking to address the problem of groundwater depletion in Iranshahr and other regions facing similar challenges. Future research in this area could explore the applicability of our approach to other regions and examine the potential economic benefits of using AGR to recharge groundwater.https://www.mdpi.com/2073-4441/15/6/1182artificial recharge zonesgroundwaterriver basinmachine learningartificial neural networks |
| spellingShingle | Mojtaba Zaresefat Reza Derakhshani Vahid Nikpeyman Amin GhasemiNejad Amir Raoof Using Artificial Intelligence to Identify Suitable Artificial Groundwater Recharge Areas for the Iranshahr Basin Water artificial recharge zones groundwater river basin machine learning artificial neural networks |
| title | Using Artificial Intelligence to Identify Suitable Artificial Groundwater Recharge Areas for the Iranshahr Basin |
| title_full | Using Artificial Intelligence to Identify Suitable Artificial Groundwater Recharge Areas for the Iranshahr Basin |
| title_fullStr | Using Artificial Intelligence to Identify Suitable Artificial Groundwater Recharge Areas for the Iranshahr Basin |
| title_full_unstemmed | Using Artificial Intelligence to Identify Suitable Artificial Groundwater Recharge Areas for the Iranshahr Basin |
| title_short | Using Artificial Intelligence to Identify Suitable Artificial Groundwater Recharge Areas for the Iranshahr Basin |
| title_sort | using artificial intelligence to identify suitable artificial groundwater recharge areas for the iranshahr basin |
| topic | artificial recharge zones groundwater river basin machine learning artificial neural networks |
| url | https://www.mdpi.com/2073-4441/15/6/1182 |
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