Deep Learning Strategies for Early Warning of Deep-seated Landslides

With the changing global climate, the incidence of geological disasters is on a steady rise year by year. Landslides, being among the most frequently occurring geological disasters, have become a global phenomenon. The occurrence of landslides inflicts significant damage upon human-made structures, river systems, and water channels, often resulting in tragic loss of life and property. Consequently, the real-time monitoring and early warning of landslides have emerged as imperative strategies to mitigate these losses. This paper draws upon two case studies: the Baishuihe landslide in the Three Gorges Reservoir area of China and the Sant’ Andrea landslide within Italy's Alpine region. It systematically explains the methodologies of landslide monitoring and prediction. By concentrating on the causal factors of landslides and combining deep learning techniques that encompass variables beyond mere rainfall, such as subterranean water levels and snow melting, this study aims to enhance the accuracy of predicting future trends in landslide movements......