Work

Remote sensing-based landslide prediction and risk assessment using a hybrid CNN–LSTM deep learning model

Year: 2026

Type: article

Abstract: Landslide susceptibility assessment is essential for risk mitigation in regions affected by complex terrain and variable environmental conditions. This study proposes a hybrid deep learning framework based on a Convolutional Neural Network and Long Short-Term Memory (CNN–LSTM) architecture to integrate spatial and temporal information for landslide susceptibility mapping using remote sensing data. Spatial features were extracted from satellite imagery and digital elevation models, while temporal patterns were characterized using rainfall and reservoir-level time series. The proposed model was applied to Kerman Province, Iran, and evaluated using an independent test dataset of historical landslide events. The CNN–LSTM model achieved an accuracy of 95.6%, an F1-score of 93.5%, and an AUC of 0.98, outperforming traditional machine learning models and standalone deep learning approaches. The resulting susceptibility maps effectively identified high-risk zones consistent with historical landslide occurrences, demonstrating the benefit of integrating spatiotemporal information for regional-scale landslide susceptibility assessment.

Source: Scientific Reports

DOI: 10.1038/s41598-026-43927-5

Authors: Fei Teng, Seyed Saeid Ekraminia, Amirreza Zarei, Yican Li

Institution: University of Tabriz

Language: English

Topic: Landslides and related hazards

Subfield: Management, Monitoring, Policy and Law

Field: Environmental Science

Domain: Physical Sciences

SDGs: Climate action, Life on land

Open Access status: gold

Estimated APC paid: $2,690