பனிப்பாறை சரிவால் ஏற்படும் திடீர் வெள்ளங்களுக்கு AI அடிப்படையிலான பல்பேரிடர் முன்னெச்சரிக்கை கட்டமைப்பு: 2026 நேபாள பேரிடரை மையமாகக் கொண்ட Remote Sensing மற்றும் Digital Twin ஆய்வு
TL;DR
This framework combines risk prediction, GIS risk mapping, and early warning layers by using Transformer/LSTM deep learning models to integrate six distinct data sources to address rising Glacial Lake Outburst Floods and avalanche-induced flash floods in the climate-sensitive Himalayas.
Abstract
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.