Hybridizing genetic random forest and self-attention based CNN-LSTM algorithms for landslide susceptibility mapping in Darjiling and Kurseong, India

authored by

Armin Moghimi, Chiranjit Singha, Mahdiyeh Fathi, Saied Pirasteh, Ali Mohammadzadeh, Masood Varshosaz, Jian Huang, Huxiong Li

Abstract

Landslides are a prevalent natural hazard in West Bengal, India, particularly in Darjeeling and Kurseong, resulting in substantial socio-economic and physical consequences. This study aims to develop a hybrid model, integrating a Genetic-based Random Forest (GA-RF) and a novel Self-Attention based Convolutional Neural Network and Long Short-term Memory (SA-CNN-LSTM), for accurate landslide susceptibility mapping (LSM) and generate landslide vulnerability-building map in these regions. To achieve this, we compiled a database with 1830 historical data points, incorporating a landslide inventory as the dependent variable and 32 geo-environmental parameters from Remote Sensing (RS) and Geographic Information Systems (GIS) layers as independent variables. These parameters include features like topography, climate, hydrology, soil properties, terrain distribution, radar features, and anthropogenic influences. Our hybrid model exhibited superior performance with an AUC of 0.92 and RMSE of 0.28, outperforming standalone SA-CNN-LSTM, GA-RF, RF, MLP, and TreeBagger models. Notably, slope, Global Human Modification (gHM), Combined Polarization Index (CPI), distances to streams and roads, and soil erosion emerged as key layers for LSM in the region. Our findings identified around 30% of the study area as having high to very high landslide susceptibility, 20% as moderate, and 50% as low to very low. The vulnerability-building map for 244,552 building footprints indicated varying landslide risk levels, with a significant proportion (27.74%) at high to very high risk. Our model highlighted high-risk zones along roads in the northeastern and southern areas. These insights can enhance landslide risk management in Darjeeling and Kurseong, guiding sustainable strategies for future damage qualification.

Organisation(s)

Ludwig-Franzius-Institute of Hydraulics, Estuarine and Coastal Engineering

External Organisation(s)

Visva-Bharati University
University of Tehran
Shaoxing University
K.N. Toosi University of Technology
Saveetha University (SIMATS)

Type

Article

Journal

Quaternary Science Advances

Volume

14

No. of pages

20

Publication date

18.04.2024

Publication status

E-pub ahead of print

Peer reviewed

Yes

ASJC Scopus subject areas

Geology, Earth-Surface Processes, Earth and Planetary Sciences (miscellaneous)

Sustainable Development Goals

SDG 13 - Climate Action

Electronic version(s)

https://doi.org/10.1016/j.qsa.2024.100187 (Access: Open)