Optimization of stochastic ANN models for intelligent flow-based prediction of soil erosion susceptibility maps (SESM)

Soil erosion, as a natural and complex geomorphological process driven by gravitational forces, wind, and water, results in the displacement and deposition of soil particles, leading to sedimentation in both natural and artificial water bodies, loss of fertile soil, and increased flood risk in vulnerable regions. This study aims to enhance the precision and robustness of soil erosion susceptibility mapping through the optimization of artificial neural networks (ANNs) using four advanced swarm intelligence algorithms: Particle Swarm Optimization (PSO), Black Hole Algorithm (BHA), Evaporation Rate Water Cycle Algorithm (ERWCA), and Vibrating Search Algorithm (VSA). The research was carried out in Kermanshah Province, Iran, integrating 14 environmental, geographical, and structural-spatial criteria that influence erosion processes. The results revealed that hybrid ANN-Swarm Intelligence models demonstrated superior predictive capability, with ERWCA-MLP and VSA-MLP achieving the best performance. The main innovation of this study lies in combining advanced metaheuristic optimization with neural network learning to enhance both the accuracy and interpretability of erosion susceptibility maps, supporting sustainable land and soil resource management.