பனிப்பாறை சரிவால் ஏற்படும் திடீர் வெள்ளங்களுக்கு AI அடிப்படையிலான பல்பேரிடர் முன்னெச்சரிக்கை கட்டமைப்பு: 2026 நேபாள பேரிடரை மையமாகக் கொண்ட Remote Sensing மற்றும் Digital Twin ஆய்வு
AI-Based Multi-Hazard Early Warning Framework for Glacier Collapse–Triggered Flash Floods: A Remote Sensing and Digital Twin Study of the 2026 Nepal Disaster
DOI:
https://doi.org/10.63300/tm12042026.15Keywords:
Glacial Lake Outburst Flood (GLOF), Artificial Intelligence, Transformer, LSTM, Remote Sensing, Digital Twin, GIS Risk MapAbstract
Exposing the technical limits of existing early warning systems after a devastating August 2026 flash flood in Nepal's Trisuli–Bhote Koshi basin, this paper proposes a multi-hazard early warning framework to address rising Glacial Lake Outburst Floods (GLOFs) and avalanche-induced flash floods in the climate-sensitive Himalayas. The framework combines risk prediction, GIS risk mapping, and early warning layers by using Transformer/LSTM deep learning models to integrate six distinct data sources: satellite imagery, glacier temperature, snowmelt rate, rainfall, seismic signals, and river water levels. Additionally, it incorporates Digital Twin technology for real-time basin modeling and "what-if" simulations, demonstrating through the 2026 Nepal disaster case study how lead times can be improved across South Asian Himalayan regions and similar high-risk zones.
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