Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
GIS and Remote Sensing-Based Multi-Criteria Analysis for Delineation of Groundwater Potential Zones: A Case Study for Industrial Zones in Bangladesh
Groundwater is a crucial natural resource that varies in quality and quantity across Bangladesh. Increased population and urbanization place enormous demands on groundwater supplies, reducing both their quality and quantity. This research aimed to delineate the groundwater potential zone in the Gazipur district, Bangladesh, by integrating eleven thematic layers. Data and information were gathered from Landsat 8, the digital elevation model, the google earth engine, and several ancillary sources. A multi-criterion decision-making (MCDM) based analytical hierarchy process (AHP) was used in a GIS platform to estimate the groundwater potential index. The potential index values were finally classified into five sub-groups: very low, low, moderate, high, and very high to generate a groundwater water potential zone (GWPZ) map. The results show that groundwater potential in about 0.002% (0.026 km2) of the area is very low, 3.83% (63.18 km2) of the area is low, 56.2% (927.05 km2) of the area is medium, 39.25% (647.46 km2) of the area is high, and the rest 0.72% (11.82 km2) of the area is very high. The validation of GWPZ maps based on the groundwater level data at 20 observation wells showed an overall accuracy of 80%. In addition, the ROC curve showed 84% accuracy of GWPZ maps when validated with water inventory points across the study region. Overall, this study presents an easy and practical approach for identifying groundwater potential zones, which may help improve planning and sustainable groundwater resource management.
GIS and Remote Sensing-Based Multi-Criteria Analysis for Delineation of Groundwater Potential Zones: A Case Study for Industrial Zones in Bangladesh
Groundwater is a crucial natural resource that varies in quality and quantity across Bangladesh. Increased population and urbanization place enormous demands on groundwater supplies, reducing both their quality and quantity. This research aimed to delineate the groundwater potential zone in the Gazipur district, Bangladesh, by integrating eleven thematic layers. Data and information were gathered from Landsat 8, the digital elevation model, the google earth engine, and several ancillary sources. A multi-criterion decision-making (MCDM) based analytical hierarchy process (AHP) was used in a GIS platform to estimate the groundwater potential index. The potential index values were finally classified into five sub-groups: very low, low, moderate, high, and very high to generate a groundwater water potential zone (GWPZ) map. The results show that groundwater potential in about 0.002% (0.026 km2) of the area is very low, 3.83% (63.18 km2) of the area is low, 56.2% (927.05 km2) of the area is medium, 39.25% (647.46 km2) of the area is high, and the rest 0.72% (11.82 km2) of the area is very high. The validation of GWPZ maps based on the groundwater level data at 20 observation wells showed an overall accuracy of 80%. In addition, the ROC curve showed 84% accuracy of GWPZ maps when validated with water inventory points across the study region. Overall, this study presents an easy and practical approach for identifying groundwater potential zones, which may help improve planning and sustainable groundwater resource management.
GIS and Remote Sensing-Based Multi-Criteria Analysis for Delineation of Groundwater Potential Zones: A Case Study for Industrial Zones in Bangladesh
Md. Mizanur Rahman (Autor:in) / Faisal AlThobiani (Autor:in) / Shamsuddin Shahid (Autor:in) / Salvatore Gonario Pasquale Virdis (Autor:in) / Mohammad Kamruzzaman (Autor:in) / Hafijur Rahaman (Autor:in) / Md. Abdul Momin (Autor:in) / Md. Belal Hossain (Autor:in) / Emad Ismat Ghandourah (Autor:in)
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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Delineation of groundwater potential zones using remote sensing and GIS-based data-driven models
| Online Contents | 2017