CNN-LSTM-ATTENTION DEEP LEARNING MODEL FOR MAPPING LANDSLIDE SUSCEPTIBILITY IN KERALA, INDIA

8 citations as recorded by crossref.

1. Hybridizing genetic random forest and self-attention based CNN-LSTM algorithms for landslide susceptibility mapping in Darjiling and Kurseong, India A. Moghimi et al. 10.1016/j.qsa.2024.100187
2. Hydrological Predictive Modeling for Indian River: Leveraging LSTM and GRU Attention Mechanisms S. Lachure & A. Tiwari 10.1007/s42979-025-04289-3
3. Comprehensive review of remote sensing integration with deep learning in landslide forecasting and future directions N. Pawar & K. Sharma 10.1007/s11069-025-07171-w
4. Slope Instability Predictor-Kerala (SLIP-K): A mobile/web Application for Landslide Hazard Prediction in Idukki, India S. Mohan et al. 10.1007/s41748-025-00840-7
5. Advanced Landslide Detection Using Machine Learning and Remote Sensing Data M. Al-Batah et al. 10.56294/dm2024.419
6. Evaluating the influence of road construction on landslide susceptibility in Saudi Arabia’s mountainous terrain: a Bayesian-optimised deep learning approach with attention mechanism and sensitivity analysis S. Alqadhi et al. 10.1007/s11356-023-31352-4
7. Deep Learning for Exploring Landslides with Remote Sensing and Geo-Environmental Data: Frameworks, Progress, Challenges, and Opportunities Q. Zhang & T. Wang 10.3390/rs16081344
8. Makine Öğrenimi Yöntemleri ile Bireylerin Kronik Hastalık Durumlarının Sınıflandırılması: Türkiye İstatistik Kurumu’nun 2023 Gelir ve Yaşam Koşulları Araştırması Üzerine Bir Uygulama Y. Gür & K. Eşidir 10.38016/jista.1444481