Towards synergistic AI-driven ensemble framework for earthquake and rainfall induced landslide risks in Sikkim Himalayas

Litan Dutta (), Niptika Jana () and Yunus Ali Pulpadan ()
Additional contact information
Litan Dutta: Indian Institute of Technology (ISM) Dhanbad
Niptika Jana: Indian Institute of Technology (ISM) Dhanbad
Yunus Ali Pulpadan: Indian Institute of Science Education and Research

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 8, No 6, 9043-9066

Abstract: Abstract Heavy rainfall and occasional powerful earthquakes are common occurrences in rocky and steep mountainous terranes. This apart, the region has heightened risk of landslides and debris flows poses a continual threat to downstream communities, creating a continual sequence of geological hazards. This study aims to bright up a potential framework of Artificial Intelligence (AI) scenario for meticulously assessing landslide risks in the Sikkim Himalayas of North-Eastern India. By leveraging regional geological features, topographical data, hydro-geomorphological factors and anthropogenic factors as hazard indicators, we initially crafted a comprehensive data processing in Geographic Information System (GIS) platform. Simultaneously, within a GIS framework, we developed an Earthquake Hazard Zoning (EHZ) map based on earthquake event magnitude and frequency data. Our analysis of the EHZ map revealed pronounced vulnerability to high-magnitude earthquakes in the northern Sikkim region, characterized by the South-Tibetan Detachment System (STDS). Conversely, the mid to south-western areas bounded by the Main Central Thrust (MCT) exhibited a heightened susceptibility to earthquakes, potentially triggering landslides. Notably, the risk of earthquakes in south-eastern Sikkim appeared relatively lower. Leveraging the EHZ map as a pivotal input, we incorporated it into the landslide susceptibility assessment process, yielding valuable insights for identifying landslide-prone areas. In constructing our AI framework for Landslide Susceptibility Map (LSM) modeling, we meticulously selected machine learning (ML) methods with the highest accuracy. Following rigorous evaluation, the ‘Random Forest’ method emerged as the most suitable, boasting 0.910 area under the Receiver Operating Characteristic curve, and an impressive model accuracy of 84.122%, surpassing alternative methods including Bayesian Network, Decision Tree, Logistic Regression, and Multi-Layer Perceptron. Moreover, we classified future landslide occurrence probabilities into distinct categories, ranging from very low to very high susceptibility indices. This approach provides stakeholders with actionable insights for targeted risk mitigation strategies. In summary, our integrated framework offers a robust methodology for advancing risk management and mitigation efforts in the Sikkim Himalayas, empowering decision-makers with the necessary tools to safeguard communities against geological surface processes and hazards.

Keywords: Landslide risk; AI/ML; Hydro-geomorphological factors; Anthropogenic factors; Sikkim Himalayas (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11069-025-07160-z 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:121:y:2025:i:8:d:10.1007_s11069-025-07160-z

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

DOI: 10.1007/s11069-025-07160-z

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 ().