Advancements, Challenges, and Future Directions in Rainfall-Induced Landslide Prediction: A Comprehensive Review


  • Do Van Vung Department of Civil Engineering, Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam
  • The Viet Tran Department of Civil Engineering, Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam
  • Nguyen Duc Ha Vietnam Institute of Geosciences and Mineral Resources, 67 Chien Thang, Van Quan, Ha Dong, Hanoi, Vietnam
  • Nguyen Huy Duong Vietnam Institute of Geosciences and Mineral Resources, 67 Chien Thang, Van Quan, Ha Dong, Hanoi, Vietnam



climate change, empirical method, landslide monitoring, machine-learning method, physical-based method, rainfall-induced landslides


Rainfall-induced landslides threaten lives and properties globally. To address this, researchers have developed various methods and models that forecast the likelihood and behavior of rainfall-induced landslides. These methodologies and models can be broadly classified into three categories: empirical, physical-based, and machine-learning approaches. However, these methods have limitations in terms of data availability, accuracy, and applicability. This paper reviews the current state-of-the-art of rainfall-induced landslide prediction methods, focusing on the methods, models, and challenges involved. The novelty of this study lies in its comprehensive analysis of existing prediction techniques and the identification of their limitations. By synthesizing a vast body of research, it highlights emerging trends and advancements, providing a holistic perspective on the subject matter. The analysis points out that future research opportunities lie in interdisciplinary collaborations, advanced data integration, remote sensing, climate change impact analysis, numerical modeling, real-time monitoring, and machine learning improvements. In conclusion, the prediction of rainfall-induced landslides is a complex and multifaceted challenge, and no single approach is universally superior. Integrating different methods and leveraging emerging technologies offer the best way forward for improving accuracy and reliability in landslide prediction, ultimately enhancing our ability to manage and mitigate this geohazard.


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How to Cite

Vung, D. V., Tran, T. V., Duc Ha, N., & Huy Duong, N. . (2023). Advancements, Challenges, and Future Directions in Rainfall-Induced Landslide Prediction: A Comprehensive Review. Journal of Engineering and Technological Sciences, 55(4), 466-478.