Year: 2025
Type: article
Abstract: Landslide disasters are among the most severe geological hazards worldwide，particularly in China，where frequent landslides have caused significant economic losses and casualties. Accurate prediction and forecasting of landslide disasters are core tasks in disaster prevention and mitigation. This paper reviews the latest progress and challenges in landslide disaster prediction and forecasting，focusing on four aspects：spatial prediction，temporal forecasting，evolution-based prediction and forecasting，and numerical prediction and forecasting models. Spatial prediction methods encompass a range of approaches，including qualitative，semi-quantitative，statistical，and machine learning methods （shallow and deep）. Although significant progress has been made，data integration and multi-scale analysis remain major challenges. In terms of temporal forecasting，the article summarizes the application status of phenomenological and empirical approaches，statistical methods，factor–threshold analyses，and physical–mechanical forecasting. It emphasizes the need to integrate landslide evolution mechanisms with multi-field monitoring data. In the field of evolution-based landslide prediction and forecasting，this paper discusses the division of landslide evolution models，stages，and states，and their importance for prediction and forecasting accuracy. The research on landslide numerical prediction and forecasting models mainly focuses on three key areas：unified criteria for identifying landslide initiation mechanisms，the development of numerical prediction and forecasting models，and the construction of integrated forecasting platforms，highlighting their potential in real-time dynamic updates and predictions. Finally，the paper looks ahead to future directions in landslide disaster prediction and forecasting research.
Source: 中国科学基金
Authors: Huiming Tang, Yunfeng Ge, Shu Zhang, Wei Hu
Language: English
Subfield: Management, Monitoring, Policy and Law
Field: Environmental Science
Domain: Physical Sciences
Open Access status: bronze
Funder: China University of Geosciences
OpenAlex ID: W7119482426