Landslide Prediction and Mapping through Geospatial and Neural Network Approach

Abstract

Identifying landslides and creating susceptibility maps are crucial in providing planners, local officials, and decision-makers with essential tools for effective disaster management strategies. The accuracy of these maps is vital in minimizing potential loss of life and property. In order to develop comprehensive landslide susceptibility mappings, it is important to consider a range of factors that encompass both terrain characteristics and meteorological conditions. Numerous advanced algorithms have been explored in the literature to enhance the precision of these maps. This study utilizes a multi-layer perceptron neural network (MLPNN) with various activation functions, including ReLU, logistic, tanh, and identity, to compare model performance and establish the most accurate and reliable model for landslide susceptibility mapping. Nine conditioning factors were analyzed, including aspect, elevation, land use/land cover, normalized difference vegetation index (NDVI), rainfall, slope, soil type, earthquake, and lithology. The performance of the models was assessed using multiple metrics, including training score, testing score, kappa coefficient, specificity, sensitivity, and Area Under the Curve (AUC). The findings indicate that the MLPNN_logistic outperformed the other models, achieving kappa and AUC values of 0.504 and 0.757, respectively, in the development of susceptibility maps. As a result, the MLPNN_logistic model is identified as the most reliable and effective tool for landslide susceptibility mapping in this study, rendering it an optimal choice for predictive analyses in this field.