Groundwater potentiality deciphering and sensitivity study using remote sensing technique and fuzzy approach

Elevating industrialization and urbanization have increased water demand, resulting in a water crisis and plummeting groundwater resources day by day. The present research proposed a model to decipher groundwater potential zones by integrating remote sensing (RS) data with fuzzy logic in an ArcGIS environment. Eleven groundwater potentiality influencing factors have been employed for the study. Each layer was passed through a multicollinearity check, resulting in no collinearity found between the layers. Furthermore, each layer was reclassified, ranked according to their potential to the groundwater occurrence, and assigned fuzzy values. The groundwater potential zones were developed by applying an overlay operation to integrate eleven fuzzy layers. According to the fuzzy value, the Surat district is divided into four potential zones: very poor, poor, moderate, and good. The result shows that 32.21% (1343 km2) and 31.63% (1319 km2) have good and moderate groundwater potential zones, respectively. Additionally, the map removal sensitivity study illustrated that drainage density, lineament density, and rainfall are more sensitive to potential zones in the study area. The potential zones have been verified by a false matrix, indicating substantial agreement between groundwater levels and potential zones with an overall accuracy of 81.1%. Thus, the integration of RS data and fuzzy-based method is an efficient method for deciphering groundwater potential zones and can be applied anywhere with necessary adjustment.