Exploring deep learning models for roadside landslide prediction: Insights and implications from comparative analysis

Abstract

This study undertakes a comparative analysis of four distinct deep learning models, i.e., Convolutional Neural Network (CNN), Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM), in the context of roadside landslide prediction, aiming to provide comprehensive insights into their strengths and weaknesses. A geospatial dataset from the Lai Châu province, Vietnam, with thirteen environmental factors (elevation, aspect, slope, curvature, topographic wetness index, stream power index, flow accumulation, geology, normalized difference vegetation index, maximum rainfall, average annual rainfall, and proximity to faults and rivers) and 284 road-along landslides was considered for analysis. Our modeling efforts yielded invaluable insights into the performance of these models during both training and validation phases. The DNN model emerged as the frontrunner in the training phase, boasting the highest area under the curve (AUC) of 0.94, accuracy of 87.47%, kappa of 0.748, and lowest RMSE of 0.125. However, during validation, the CNN model outshone others, exhibiting the highest AUC of 0.88 and overall accuracy of 80.00%. Despite variations in performance metrics across phases, CNN consistently demonstrated robust predictive prowess. The findings of this study underscore the significance of selecting appropriate machine learning models tailored to specific contexts and objectives. Moreover, they contribute valuable insights for decision-makers and researchers alike, ultimately aiming to enhance the safety and resilience of communities inhabiting landslide-prone areas. Moving forward, future research directions may explore ensemble methods, novel architectures, and interpretability techniques to further advance predictive accuracy and applicability in roadside landslide susceptibility modeling.