Groundwater potential zone mapping using GIS and Remote Sensing based models for sustainable groundwater management
AbstractThe present research is conducted in the southern region of Khyber Pakhtunkhwa, Pakistan, to identify groundwater potential zones (GWPZ). We used three models including Weight of Evidence (WOE), Frequency Ratio (FR), and Information Value (IV) with twelve parameters (elevation, slope, aspect...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-01-01
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| Series: | Geocarto International |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2024.2306275 |
| _version_ | 1797315602341167104 |
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| author | Abdur Rehman Fakhrul Islam Aqil Tariq Ijaz Ul Islam Davis Brian J Tehmina Bibi Waqar Ahmad Liaqat Ali Waseem Shankar Karuppannan Saad Al-Ahmadi |
| author_facet | Abdur Rehman Fakhrul Islam Aqil Tariq Ijaz Ul Islam Davis Brian J Tehmina Bibi Waqar Ahmad Liaqat Ali Waseem Shankar Karuppannan Saad Al-Ahmadi |
| author_sort | Abdur Rehman |
| collection | DOAJ |
| description | AbstractThe present research is conducted in the southern region of Khyber Pakhtunkhwa, Pakistan, to identify groundwater potential zones (GWPZ). We used three models including Weight of Evidence (WOE), Frequency Ratio (FR), and Information Value (IV) with twelve parameters (elevation, slope, aspect, curvature, drainage network, LULC, precipitation, geology, Lineament, NDVI, road, and soil texture, that have been prepared and integrated into ArcGIS 10.8. The reliability of the applied models’ results was validated using Area Under the Receiver Operating Characteristics (AUROC). The GWPZ were reclassified into five classes, i.e. very low, low, medium, high, and very high zone. The area occupied by mentioned classes using WOE are very low (10.14%), low (19.58%), medium (26.75%), high (27.10%), very high (16.40%), while using FR are very low (20.93%), low (32.38%), medium (18.92%), high (13.13%), very high (14.61%) and using IV are very low (14.41%), low (17.17%), medium (29.01%), high (25.85%), and very High (13.53%). The Success Rate Curve of WOE, FR, and IV were 0.86, 0.91, and 0.87, while the Predicted Rate Curve values were 0.89, 0.93, and 0.90, respectively. The results revealed that all applied statistical models performed very well to delineate GWPZ. However, use of the FR technique is strongly encouraged to evaluate the GWPZ, and its findings are especially useful for managing groundwater resources in urban planning. Our approaches for assessing the GWPZ mapping can be applied in any region with similar scenarios and are recommended as a helpful tool for policymakers to manage groundwater. |
| first_indexed | 2024-03-08T03:06:07Z |
| format | Article |
| id | doaj.art-9308e432549545188a1238347a9cdb88 |
| institution | Directory Open Access Journal |
| issn | 1010-6049 1752-0762 |
| language | English |
| last_indexed | 2024-03-08T03:06:07Z |
| publishDate | 2024-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj.art-9308e432549545188a1238347a9cdb882024-02-13T09:15:37ZengTaylor & Francis GroupGeocarto International1010-60491752-07622024-01-0139110.1080/10106049.2024.2306275Groundwater potential zone mapping using GIS and Remote Sensing based models for sustainable groundwater managementAbdur Rehman0Fakhrul Islam1Aqil Tariq2Ijaz Ul Islam3Davis Brian J4Tehmina Bibi5Waqar Ahmad6Liaqat Ali Waseem7Shankar Karuppannan8Saad Al-Ahmadi9College of Hydrology and Water Resources, Hohai University, Nanjing, ChinaDepartment of Geology, Khushal Khan Khattak University, Karak, PakistanDepartment of Wildlife, Fisheries and Aquaculture, Mississippi State University, Mississippi State, MS, USADepartment of computer science, Abdul Wali Khan University, Mardan, PakistanDepartment of Wildlife, Fisheries and Aquaculture, Mississippi State University, Mississippi State, MS, USAInstitute of Geology, The University of Azad Jammu & Kashmir, Muzaffarabad, PakistanGeological resources and Geoengineering, Changan University, Xi’an, ChinaDepartment of Geography, Government College University Faisalabad, Faisalabad, Punjab, PakistanDepartment of Applied Geology, School of Applied Nartural Sciences, Adama Science and Technology University, Adama, EthiopiaDepartment of computer science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaAbstractThe present research is conducted in the southern region of Khyber Pakhtunkhwa, Pakistan, to identify groundwater potential zones (GWPZ). We used three models including Weight of Evidence (WOE), Frequency Ratio (FR), and Information Value (IV) with twelve parameters (elevation, slope, aspect, curvature, drainage network, LULC, precipitation, geology, Lineament, NDVI, road, and soil texture, that have been prepared and integrated into ArcGIS 10.8. The reliability of the applied models’ results was validated using Area Under the Receiver Operating Characteristics (AUROC). The GWPZ were reclassified into five classes, i.e. very low, low, medium, high, and very high zone. The area occupied by mentioned classes using WOE are very low (10.14%), low (19.58%), medium (26.75%), high (27.10%), very high (16.40%), while using FR are very low (20.93%), low (32.38%), medium (18.92%), high (13.13%), very high (14.61%) and using IV are very low (14.41%), low (17.17%), medium (29.01%), high (25.85%), and very High (13.53%). The Success Rate Curve of WOE, FR, and IV were 0.86, 0.91, and 0.87, while the Predicted Rate Curve values were 0.89, 0.93, and 0.90, respectively. The results revealed that all applied statistical models performed very well to delineate GWPZ. However, use of the FR technique is strongly encouraged to evaluate the GWPZ, and its findings are especially useful for managing groundwater resources in urban planning. Our approaches for assessing the GWPZ mapping can be applied in any region with similar scenarios and are recommended as a helpful tool for policymakers to manage groundwater.https://www.tandfonline.com/doi/10.1080/10106049.2024.2306275AUROCbivariate modelsgeospatial technologygroundwater potential zonesSentinel-2 |
| spellingShingle | Abdur Rehman Fakhrul Islam Aqil Tariq Ijaz Ul Islam Davis Brian J Tehmina Bibi Waqar Ahmad Liaqat Ali Waseem Shankar Karuppannan Saad Al-Ahmadi Groundwater potential zone mapping using GIS and Remote Sensing based models for sustainable groundwater management Geocarto International AUROC bivariate models geospatial technology groundwater potential zones Sentinel-2 |
| title | Groundwater potential zone mapping using GIS and Remote Sensing based models for sustainable groundwater management |
| title_full | Groundwater potential zone mapping using GIS and Remote Sensing based models for sustainable groundwater management |
| title_fullStr | Groundwater potential zone mapping using GIS and Remote Sensing based models for sustainable groundwater management |
| title_full_unstemmed | Groundwater potential zone mapping using GIS and Remote Sensing based models for sustainable groundwater management |
| title_short | Groundwater potential zone mapping using GIS and Remote Sensing based models for sustainable groundwater management |
| title_sort | groundwater potential zone mapping using gis and remote sensing based models for sustainable groundwater management |
| topic | AUROC bivariate models geospatial technology groundwater potential zones Sentinel-2 |
| url | https://www.tandfonline.com/doi/10.1080/10106049.2024.2306275 |
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