Deciphering of groundwater potential zones in hard rock terrain using GIS technology with AHP statistical methods: A case study of Nilgiri, Tamil Nadu, India.

This research aims to define the potential of using natural networks for groundwater mapping. While neural networks have proven effective for various perceptual tasks, the difficulty in identifying data points below the surface remains a key challenge. The area under study encompasses a mountainous region in the Western Ghats. The most efficient, practical, along sensible methods for defining the GWPZ (Groundwater Potential Zones) in the Nilgiri's hard rock terrain are Geographic Information Systems (GIS) as well as analytic hierarchy process (AHP) of multicriteria decision making. To create various thematic layers, we utilized Indian topographical maps, satellite imagery, and field observations. We collected data on ten factors influencing groundwater(GW), including LULC(Land Use Land Cover), elevation, slope, soil type, geomorphology(GM), rainfall(RF), geology(GL), LD(lineament density), as well as DD(drainage density). Based on the weight assignment, all the thematic maps influencing GW events were assessed and compiled using GIS analysis. The weighted index overlay (WIO) approach and PCM (pairwise comparison matrix) within the AHP were used for a hierarchical ranking to identify the possible GW zones. The outcome revealed that the sample region could be divided into 5 separate groundwater potential (GWP) areas, i.e., very good (10%), good (32%), moderate (21%), low (26%), as well as very low (11%) potentials. Well and spring data were used to validate the model, and the ROC (Receiver Operating Characteristic) curve method was applied. The results showed a good accuracy of 70.03%. chance of correctly distinguishing a randomly chosen true positive from a false positive. This research is useful for improved preparation and control of GW supplies and offers swift guidelines for the discovery of GW in the hard rock aquifer region.