Multi-resource potentiality and multi-hazard susceptibility assessments of the central west coast of India applying machine learning and geospatial techniques

Abstract

The understating of natural resources and hazards is very fundamental from spatial perspective to reduce loss of resource and human lives. For this, the main aim of the current research is to synthesize the three natural resources (groundwater, wetland, and forest) into one multi-resource (MR) potentiality map and three natural hazards (flood, landslide, and shoreline erosion) into one multi-hazard (MH) susceptibility map. To achieve this goal, several inventories, geo-environmental factors, satellite bands, and indices have been taken as input data sets. Random forest (RF) model has been considered for individual mapping (except shoreline erosion mapping) due to its high precision. For shoreline erosion mapping, digital shoreline analysis system (DSAS) model has been employed. The accuracies of the model have been determined from the area under receiver operating characteristic (AUROC) curve, producer accuracy (PA), and user accuracy (UA). RF model has more than 90% prediction accuracy in different resource and hazard mapping, whereas for the forest mapping, PA and UA are 90% and 80%, respectively. The results of this study for multi-resource mapping show forest (24.05%) and groundwater (22.28%) as the major resources, whereas flood (17.27%) is the most destructive in comparison with other hazards. The multi-resource and multi-hazard maps of the research area provide an important tool to land managers and policymakers for sustainable development and management.