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September 21, 2025Open Access

AI-Powered Flood Risk Assessment for Gilgit-Baltistan Using Multi-Source Satellite Data and Machine Learning

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ZAzahid abbas

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Implication

AI-powered flood risk assessment identifies high-risk areas in Gilgit-Baltistan, suggesting effective disaster management strategies.

Key Points

  • The framework achieved 88.9% accuracy in classifying flood risk in Gilgit-Baltistan, marking significant progress in understanding mountainous flood vulnerabilities.
  • Integration of multiple satellite datasets allowed for the analysis of key factors like precipitation and water occurrence, emphasizing their predictive power in risk classification.
  • Utilizing a random forest classifier, the study provided district-level risk assessments, revealing that Astore, Diamer, and Nagar face the highest flood risks.
  • This scalable framework suggests a new approach to flood management and early warning systems in mountainous regions, contributing to climate adaptation efforts.

Cite This Study

zahid abbas (2025) studied this question.

synapsesocial.com/papers/68d43c8e713b0b5dfea7c847https://doi.org/10.31223/x5k74m
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