Modelling environmental fragility in an industrial landscape: An integrated remote sensing-GIS framework for sustainable planning in Brahmani-Dwarka Interfluve

• AHP and FAHP-based PEF models indicate high fragility zones cover 23.96% and 22.38%. • AHP and FAHP-based EEF models show high fragility zones occupy 16.80% and 27.85%. • Environmentally fragile hotspots cover 25.49% and 24.01% in AHP and FAHP PEF models. • Environmentally fragile hotspots covered 21.07% and 31.46% in AHP and FAHP EEF models. • FAHP outperformed AHP in ROC validation, showing better accuracy and reliability. Evaluating environmental vulnerability is crucial for efficient and sustainable management, particularly in regions that are ecologically sensitive and experiencing rapid transformations. Thus, the main goal of this study is to evaluate the environmental fragility of the Brahmani-Dwarka Interfluve by integrating key natural and human-induced factors through the analytical hierarchy process (AHP) and fuzzy analytical hierarchy process (FAHP) within a remote sensing and geographic information systems (RS-GIS) framework. To achieve this, ten unique factors influencing potential environmental fragility (PEF) were analyzed, along with four new factors to tackle emerging fragility (EEF). This study holds significant value as it focuses on a region profoundly affected by stone crushing industries, which encounter considerable threats of environmental harm. The results of the PEF model showed that high and very high fragility zones accounted for 23.96% and 22.38% of the area, according to the AHP and FAHP models. Similarly, EEF models highlighted a growing fragility, with FAHP and AHP indicating that 27.85% and 16.80% of the region were categorized as having high and very high fragility. Hotspot analysis indicated that environmentally fragile hotspots constituted 21.07% and 31.46% of the area in the AHP and FAHP-based models, respectively. ROC validation indicated that FAHP demonstrated superior performance compared to AHP in both PEF and EEF models, highlighting its exceptional predictive accuracy and reliability. The observed negligible difference between high and low sensitive parameters was 0.866% in AHP and 0.532% in FAHP, indicating the models’ reliability. The findings of the study will help policymakers, planners, and stakeholders to develop environmentally resilient strategies and policies as well as achieve sustainability in the study region.