Monitoring groundwater potential dynamics of north-eastern Bengal Basin in Bangladesh using AHP-Machine learning approaches
Groundwater is a vital natural resource that plays a critical role in sustaining agriculture, forest ecosystems, industry, and household uses. However, due to natural and anthropogenic factors, groundwater is facing alarming declines. Therefore, this study aimed to assess the potential groundwater z...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X23010282 |
| _version_ | 1827816049555275776 |
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| author | Biplob Dey Kazi Al Muqtadir Abir Romel Ahmed Mohammed Abdus Salam Mohammad Redowan Md. Danesh Miah Muhammad Anwar Iqbal |
| author_facet | Biplob Dey Kazi Al Muqtadir Abir Romel Ahmed Mohammed Abdus Salam Mohammad Redowan Md. Danesh Miah Muhammad Anwar Iqbal |
| author_sort | Biplob Dey |
| collection | DOAJ |
| description | Groundwater is a vital natural resource that plays a critical role in sustaining agriculture, forest ecosystems, industry, and household uses. However, due to natural and anthropogenic factors, groundwater is facing alarming declines. Therefore, this study aimed to assess the potential groundwater zones (PGWZ) in the north-eastern Bengal Basin of Bangladesh between 1990 and 2021 using satellite images, public and field data pertaining to ten environmental parameters. The study utilized analytical hierarchy process to identify PGWZ and evaluated the effectiveness of machine learning (ML) algorithms (K-nearest neighbors, support vector machine, XGBoost, decision tree, and random forest) for PGWZ classification. The findings indicated a decline in groundwater potential over the decades, which was categorized into five distinct zones based on the relative groundwater potential. The very high PGWZ decreased from 2.19% to 1.3%, and high PGWZ from 34.57% to 28.24%, while there was a sharp increase in the poor status of PGWZ (very low, low, and medium zones) over the same periods. The accuracy and kappa coefficients of the ground data validation for the estimated PGWZ map were 84.34% and 79.61%, respectively. According to accuracy, precision, recall, and f1-score, five ML models are reliable predictors of PGWZ. RF achieved the highest accuracy of 92.33%, while XGBoost achieved an accuracy of 90.31%. Both models demonstrated superior prediction performance for PGWZ based on the normalized leverage factor. The study attributes the alteration of groundwater potential to changes in land use and land covers, increased land surface temperatures, decreased rainfall, and changes in soil erosion in the study region over the three decades. The results of this study offer valuable insights for decision-makers to make informed decisions for the sustainable and responsible management of groundwater resources. |
| first_indexed | 2024-03-12T00:09:23Z |
| format | Article |
| id | doaj.art-a0a3358b04a3414c8edcab30b94f056d |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-03-12T00:09:23Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-a0a3358b04a3414c8edcab30b94f056d2023-09-16T05:30:18ZengElsevierEcological Indicators1470-160X2023-10-01154110886Monitoring groundwater potential dynamics of north-eastern Bengal Basin in Bangladesh using AHP-Machine learning approachesBiplob Dey0Kazi Al Muqtadir Abir1Romel Ahmed2Mohammed Abdus Salam3Mohammad Redowan4Md. Danesh Miah5Muhammad Anwar Iqbal6Department of Forestry and Environmental Science, Shahjalal University of Science and Technology, Sylhet 3114, BangladeshDepartment of Forestry and Environmental Science, Shahjalal University of Science and Technology, Sylhet 3114, BangladeshDepartment of Forestry and Environmental Science, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh; Corresponding author.Department of Environmental Science and Disaster Management, Noakhali Science and Technology University, Noakhali 3814, BangladeshDepartment of Forestry and Environmental Science, Shahjalal University of Science and Technology, Sylhet 3114, BangladeshInstitute of Forestry and Environmental Science, University of Chittagong, Chittagong 4331, BangladeshDepartment of Environmental Science and Disaster Management, Noakhali Science and Technology University, Noakhali 3814, BangladeshGroundwater is a vital natural resource that plays a critical role in sustaining agriculture, forest ecosystems, industry, and household uses. However, due to natural and anthropogenic factors, groundwater is facing alarming declines. Therefore, this study aimed to assess the potential groundwater zones (PGWZ) in the north-eastern Bengal Basin of Bangladesh between 1990 and 2021 using satellite images, public and field data pertaining to ten environmental parameters. The study utilized analytical hierarchy process to identify PGWZ and evaluated the effectiveness of machine learning (ML) algorithms (K-nearest neighbors, support vector machine, XGBoost, decision tree, and random forest) for PGWZ classification. The findings indicated a decline in groundwater potential over the decades, which was categorized into five distinct zones based on the relative groundwater potential. The very high PGWZ decreased from 2.19% to 1.3%, and high PGWZ from 34.57% to 28.24%, while there was a sharp increase in the poor status of PGWZ (very low, low, and medium zones) over the same periods. The accuracy and kappa coefficients of the ground data validation for the estimated PGWZ map were 84.34% and 79.61%, respectively. According to accuracy, precision, recall, and f1-score, five ML models are reliable predictors of PGWZ. RF achieved the highest accuracy of 92.33%, while XGBoost achieved an accuracy of 90.31%. Both models demonstrated superior prediction performance for PGWZ based on the normalized leverage factor. The study attributes the alteration of groundwater potential to changes in land use and land covers, increased land surface temperatures, decreased rainfall, and changes in soil erosion in the study region over the three decades. The results of this study offer valuable insights for decision-makers to make informed decisions for the sustainable and responsible management of groundwater resources.http://www.sciencedirect.com/science/article/pii/S1470160X23010282Groundwater potential zonesBengal BasinGeospatial techniqueMachine learningAHPBangladesh |
| spellingShingle | Biplob Dey Kazi Al Muqtadir Abir Romel Ahmed Mohammed Abdus Salam Mohammad Redowan Md. Danesh Miah Muhammad Anwar Iqbal Monitoring groundwater potential dynamics of north-eastern Bengal Basin in Bangladesh using AHP-Machine learning approaches Ecological Indicators Groundwater potential zones Bengal Basin Geospatial technique Machine learning AHP Bangladesh |
| title | Monitoring groundwater potential dynamics of north-eastern Bengal Basin in Bangladesh using AHP-Machine learning approaches |
| title_full | Monitoring groundwater potential dynamics of north-eastern Bengal Basin in Bangladesh using AHP-Machine learning approaches |
| title_fullStr | Monitoring groundwater potential dynamics of north-eastern Bengal Basin in Bangladesh using AHP-Machine learning approaches |
| title_full_unstemmed | Monitoring groundwater potential dynamics of north-eastern Bengal Basin in Bangladesh using AHP-Machine learning approaches |
| title_short | Monitoring groundwater potential dynamics of north-eastern Bengal Basin in Bangladesh using AHP-Machine learning approaches |
| title_sort | monitoring groundwater potential dynamics of north eastern bengal basin in bangladesh using ahp machine learning approaches |
| topic | Groundwater potential zones Bengal Basin Geospatial technique Machine learning AHP Bangladesh |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X23010282 |
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