Enhancing Landslide Susceptibility Prediction Using Deep Learning Through Optimal Activation and Loss Function Selection

Abstract

Landslide susceptibility prediction has increasingly shifted from traditional statistical methods toward deep learning approaches capable of modeling complex nonlinear relationships among geo-environmental controlling factors. However, model performance is influenced not only by network architecture but also by the selection of activation and loss functions, which directly affect learning dynamics and generalization capability. This study systematically investigates the influence of activation–loss function combinations within a unified deep learning framework for landslide susceptibility mapping in Sin Ho District, northwestern Vietnam. A multi-source geospatial database integrating topographic, geological, hydrological, land-use, seismic, and rainfall-related factors was used to train and validate multiple model configurations. Model performance was evaluated using a comprehensive set of quantitative indicators, including area under the ROC curve (AUC) and accuracy (ACC). The results indicate that models employing the Softmax activation function consistently achieve superior predictive performance and robustness. Among all tested configurations, the Softmax–Kullback–Leibler Divergence (SM–KLD) model delivered the most reliable results, attaining a validation AUC of 0.878, and ACC of 83.42, demonstrating strong discrimination ability, stable generalization, and reduced prediction error. The landslide susceptibility map generated using the optimal SM–KLD configuration exhibits a clear and progressive increase in landslide density from very low to very high susceptibility classes, confirming meaningful spatial stratification of landslide hazard. These findings highlight that careful selection of activation and loss functions can substantially enhance deep learning performance without increasing model complexity or computational cost, providing a robust and efficient framework for landslide susceptibility assessment and disaster-risk reduction in mountainous regions under changing climatic conditions.