Geological disaster risk assessment based on remote sensing, GIS, and a multi-strategy improved GWO-optimized Elman neural network

Abstract

To improve the accuracy and intelligence of geological disaster risk assessment, this study proposes a novel method based on a Multi-strategy Improved Grey Wolf Optimization algorithm combined with the Elman Neural Network (MIGWO-Elman). The model utilizes spatial factors such as NDVI extracted from remote sensing imagery, and slope and aspect derived from DEM, integrated with multi-source data including geological and rainfall information. A regional feature database is constructed using GIS techniques. In the model design, strategies including chaotic reverse learning, nonlinear convergence control, Levy flight perturbation, and self-history best are introduced to enhance the global search capability and convergence stability of the algorithm. Wenchuan County is selected as the study area for experimental validation. Results show that the MIGWO-Elman model outperforms other comparative models in terms of accuracy (91.76%), recall (93.57%), F1 score (0.919), Kappa coefficient (0.835), and AUC value (0.948). Moreover, 93.38% of known disaster points fall within high and very high-risk zones, demonstrating a strong agreement with actual disaster distribution. This study confirms that the proposed model, supported by remote sensing and GIS, can provide effective technical support for geological disaster risk zoning and emergency management on digital earth platforms.