High-Precision Landslide Susceptibility Mapping Using CNN-LSTM-Attention Models
Received: 15 April 2025 | Revised: 29 April 2025 and 12 May 2025 | Accepted: 17 May 2025 | Online: 30 June 2025
Corresponding author: D. Anil
Abstract
In areas with vulnerable terrain, landslides pose a significant risk to both lives and infrastructure, making precise susceptibility maps essential for effective risk management and planning. This research introduces an advanced Deep Learning (DL) framework that combines multiple models, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), Attention U-Net, and their combinations (CNN + LSTM and CNN + Attention), to enhance the landslide susceptibility prediction. The models were trained and validated using 1,580 landslide occurrence data points from the Western Ghats of Karnataka, with 17 geospatial conditioning factors derived from satellite remote sensing and processed through Google Earth Engine (GEE). To verify the reliability of the input features, multicollinearity was evaluated using VIF and TOL. A comparative analysis showed that the LSTM model achieved the highest accuracy at 98.80% and an Area Under the Curve (AUC) of 0.988, with CNN + Attention U-Net performing similarly. The CNN + LSTM hybrid model demonstrated an exceptional spatial detection ability, identifying the highest proportion of landslide risk areas. The generated susceptibility map offers a reliable, data-driven resource for early warning systems, land-use planning, and disaster resilience strategies. This map provides practical insights for the government agencies, urban planners, and disaster response teams to develop proactive hazard mitigation measures.
Keywords:
landslide susceptibility, deep learning, convolutional neural networks, long short-term memory, google earth engine, remote sensingDownloads
References
D. Nseka et al., "The damage caused by landslides in socio-economic spheres within the Kigezi highlands of South Western Uganda," Environmental & Socio-economic Studies, vol. 9, no. 1, pp. 23–34, Mar. 2021.
D. Khadka, J. Zhang, and A. Sharma, "Geographic object-based image analysis for landslide identification using machine learning on google earth engine," Environmental Earth Sciences, vol. 84, no. 3, Jan. 2025, Art. no. 92.
N. M. Khassaf and N. H. M. Ali, "Improving Pre-trained CNN-LSTM Models for Image Captioning with Hyper-Parameter Optimization," Engineering, Technology & Applied Science Research, vol. 14, no. 5, pp. 17337–17343, Oct. 2024.
J. Zhang, J. Qian, Y. Lu, X. Li, and Z. Song, "Study on Landslide Susceptibility Based on Multi-Model Coupling: A Case Study of Sichuan Province, China," Sustainability, vol. 16, no. 16, Aug. 2024, Art. no. 6803.
L. Liu and J. Deng, "Multi-model combination in key steps for landslide susceptibility modeling and uncertainty analysis: a case study in Baoji City, China," Geomatics, Natural Hazards and Risk, vol. 15, no. 1, Dec. 2024, Art. no. 2344804.
Y. Song et al., "Landslide susceptibility assessment through multi-model stacking and meta-learning in Poyang County, China," Geomatics, Natural Hazards and Risk, vol. 15, no. 1, Dec. 2024, Art. no. 2354499.
H. Yang et al., "Advancing flood susceptibility modeling using stacking ensemble machine learning: A multi-model approach," Journal of Geographical Sciences, vol. 34, no. 8, pp. 1513–1536, Aug. 2024.
B. An et al., "Landslide Susceptibility Mapping Based on Ensemble Learning in the Jiuzhaigou Region, Sichuan, China," Remote Sensing, vol. 16, no. 22, Nov. 2024, Art. no. 4218.
T. Chaithong, M. Sasingha, and S. Phakdimek, "Spatial prediction of changes in landslide susceptibility under extreme daily rainfall from the cmip6 multi-model ensemble," Theoretical and Applied Climatology, vol. 155, no. 7, pp. 6771–6795, Jul. 2024.
B. Li, C. Li, Y. Liu, J. Tan, P. Feng, and W. Yao, "Harnessing Distributed Deep Learning for Landslide Displacement Prediction: A Multi-Model Collaborative Approach Amidst Data Silos," Journal of Earth Science, vol. 35, no. 5, pp. 1770–1775, Oct. 2024.
N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore, "Google Earth Engine: Planetary-scale geospatial analysis for everyone," Remote Sensing of Environment, vol. 202, pp. 18–27, Dec. 2017.
D. Das et al., "Assessment of landslide susceptibility in the Himalayan state of Tripura, India, using a Multi-Model Approach," Current World Environment, vol. 19, no. 2, pp. 883–901, Sep. 2024.
A. L. Achu et al., "Improved flood risk assessment using multi-model ensemble machine-learning techniques in a tropical river basin of Southern India," Physical Geography, vol. 46, no. 2, pp. 127–155, Mar. 2025.
B. T. Pham et al., "Landslide susceptibility mapping using state-of-the-art machine learning ensembles," Geocarto International, vol. 37, no. 18, pp. 5175–5200, Sep. 2022.
S. K. Jagatheesaperumal, Q.-V. Pham, R. Ruby, Z. Yang, C. Xu, and Z. Zhang, "Explainable AI Over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions," IEEE Open Journal of the Communications Society, vol. 3, pp. 2106–2136, 2022.
S. K. Jagatheesaperumal, S. E. Bibri, S. Ganesan, and P. Jeyaraman, "Artificial Intelligence for road quality assessment in smart cities: a machine learning approach to acoustic data analysis," Computational Urban Science, vol. 3, no. 1, Sep. 2023, Art. no. 28.
I. Ahmad et al., "Communications Security in Industry X: A Survey," IEEE Open Journal of the Communications Society, vol. 5, pp. 982–1025, 2024.
D. Anil, S. Hiremath, S. H. Manjula, and K. R. Venugopal, "A Comprehensive Study of Machine Learning and Deep Learning Methods for Landslide Susceptibility Mapping," Tuijin Jishu/Journal of Propulsion Technology, vol. 44, pp. 1924-1930, 2023.
D. Anil, S. Hiremath, and S. H. Manjula, "Detection of Efficient Landslide Inventory Mapping in Western Ghats regions of Karnataka, India," in 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS), Apr. 2024, vol. 1, pp. 1–6.
D. Anil, and S. H. Manjula, "A Multi-Classifier Approach to Landslide Susceptibility Mapping Using Geospatial Data and Machine Learning in the Ghats Regions of Karnataka," in 2024 5th International Conference on Communication, Computing & Industry 6.0 (C2I6), Sep. 2024, pp. 1–6.
Downloads
How to Cite
License
Copyright (c) 2025 D. Anil, S. H. Manjula
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
