Citation

S. S, S. s. VC, Shaji E. Earth Sci. Inform. 2022; 15(4): 2063-2090.

Copyright

(Copyright © 2022, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s12145-022-00889-2

PMID

unavailable

Abstract

The WHO (World Health Organization) study reports that, between 1998-2017, 4.8 million people have been affected by landslides with more than 18000 deaths. The prevailing climate change and erratic high intensity rainfall are expected to trigger more landslides, which can increase the death rates per year in future. Therefore, evolving successful mechanisms to identify and predict landslides is critical in risk reduction and post-disaster management activities. With the applications of Machine Learning , the success rates of landslide identification have been improved significantly. This review paper presents the results of data analysis on the papers published for the last three decades on varying degrees of reliability and success rate on the theme "Machine Learning for landslide identification, mitigation, and prediction". The analyses show how the reliability and accuracy of the landslide prediction model have improved considerably with the tools available in Machine Learning. Though many conventional tools such as statistical packages are available, the Machine Learning algorithms gave a robust dimension for a reliable landslide risk analysis, modeling prediction tools and post-disaster damage identification, This paper recommends a multi-modal framework for characterising landslides in all aspects using Machine Learning techniques that could outperform single modal approaches. Open research problems and future research dimensions by integrating landsat data and Machine Learning for landslide studies are also discussed in this work which would be beneficial for researchers in this field and also to the community at large across the globe.

Language: en

Keywords

Artificial intelligence; Convolutional neural networks; Landslide detection; Landslide susceptibility assessment; Machine learning; Multimodal data fusion; Remote sensing