Review on the progress and future prospects of geological disasters prediction in the era of artificial intelligence

Xiang Zhang (), Minghui Zhang, Xin Liu, Berhanu Keno Terfa (), Won-Ho Nam (), Xihui Gu (), Xu Zhang (), Chao Wang (), Jian Yang (), Peng Wang (), Chenghong Hu (), Wenkui Wu () and Nengcheng Chen ()
Additional contact information
Xiang Zhang: China University of Geosciences
Minghui Zhang: China University of Geosciences
Xin Liu: China University of Geosciences
Berhanu Keno Terfa: Addis Ababa University
Won-Ho Nam: Hankyong National University
Xihui Gu: China University of Geosciences
Xu Zhang: China University of Geosciences
Chao Wang: Wuhan University
Jian Yang: Information Engineering University
Peng Wang: GAEA Space Time Co., Ltd
Chenghong Hu: AutoNavi Software Co., Ltd
Wenkui Wu: China University of Geosciences
Nengcheng Chen: China University of Geosciences

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2024, vol. 120, issue 13, No 1, 11485-11525

Abstract: Abstract Geological disasters such as landslide, debris flow and collapse are major natural disasters faced by both China and the world, which seriously threaten people’s lives, property security and the socio-economic development. Although the method of using the paradigm of traditional mathematical statistics and physical model to predict the low-probability events of geological disasters have been developed for decades, the difficulty of accurate prediction still remains significant, which is recognized as a major and urgent scientific challenge in the field of Earth science. Artificial intelligence is an important driving force for a new round of scientific and technological revolution and industrial transformation. However, how to systematically establish the AI prediction paradigm for low-probability events of geological disasters and deeply coupled with the physical mechanisms of geological disaster evolution and AI learning models still remains as a scientific bottleneck at the intersection of Earth science and information science. In order to clarify the latest research progress of AI prediction of geological disasters such as landslide, collapse and debris flow, this paper first quantifies the current status of global geological disasters and the urgency of prediction, and then summarizes the overall methodology of AI prediction of geological disasters. In particular, prediction feature selection, data set collection and AI prediction models have been detailly reviewed. Moreover, this review discussed the approaches in establishing the physical-informed AI model for higher accurate, robust, and explainable prediction performance. Subsequently, this paper summarizes the recent research achievements of AI prediction for landslide, collapse, and debris flow. Based on these progresses, we also analyzed the existing problems in the field of AI prediction of geological disasters, and indicated the key directions of AI prediction of geological disasters in the future. This review work is believed to be a critical guidance for future intelligent prediction on the severe geological disasters.

Keywords: Landslide; Prediction; Artificial intelligent; Geology (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11069-024-06673-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:120:y:2024:i:13:d:10.1007_s11069-024-06673-3

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-024-06673-3

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().