Bayesian-Informed Hybrid Deep Learning for GLOF Susceptibility and Hazard Escalation in the HKKH Region (2010–2020)

Abstract. Rapid glacier retreat, complicated geography, and unpredictable weather make the Hindu Kush–Karakoram–Himalaya (HKKH) region extremely susceptible to glacial lake outburst floods (GLOFs). Current GLOF susceptibility assessments seldom take temporal dynamics or uncertainty into account, instead concentrating on either upstream lake conditions or downstream repercussions. An integrated, uncertainty-aware GLOF susceptibility framework that combines hybrid deep learning models with Bayesian probabilistic classification is presented in this paper. Spatiotemporal variations in glacial lakes and downstream terrain are captured using multi-temporal Landsat (2010–2016) and Sentinel-2 (2016–2020) imagery, SRTM DEM, Randolph Glacier & ICIMOD Inventory, morphological, hydrological, spatial, and topographical variables, and recorded GLOF events. CNN-LSTM, CNN-RNN, and Transformer-CNN models are trained using probabilistic labels produced by Bayesian inference. With AUC values between 0.90 and 0.92, the models demonstrate high predictive performance. High-altitude northern and central HKKH regions are becoming more vulnerable due to increased glacier melt, according to hazard escalation maps from 2010 to 2020. For regional GLOF risk assessment and disaster risk management, this framework offers a scalable tool.