Synapse
⌘+K
Synapse
PulseExploreJournal ClubResearchersJournals
Instagram
HomeJournal ClubExplore
April 10, 2026Scientific ReportsOpen Access

An explainable GeoAI framework for spatial assessment of wildfire susceptibility in the Upper Ravi sub-basin, Indian Himalaya

View Full Paper
Ask AI
Bookmark
Share

Authors

SSuhebMNMd NawazuzzohaSASajid Ali

Discussion

Loading...

Member takes

Overview

Maps wildfire susceptibility in the Upper Ravi sub-basin, highlighting key environmental factors.

Key Points

  • The study aims to assess and map wildfire susceptibility in the Upper Ravi sub-basin using advanced machine learning techniques.
  • Integrated GIS and remote sensing data with machine learning algorithms.
  • Evaluated five algorithms, including Random Forest and XGBoost.
  • Conducted SHAP, Monte Carlo uncertainty, and Sobol sensitivity analyses on the stacking model.
  • The stacking ensemble model achieved an AUC of 0.95, indicating strong predictive performance.
  • Approximately 20.75% of the area is classified as high to very high wildfire susceptibility zones.
  • Soil moisture and temperature were identified as the most influential factors affecting wildfire risk.

Cite This Study

Suheb et al. (2026) studied this question.

synapsesocial.com/papers/69d894526c1944d70ce05346https://doi.org/10.1038/s41598-026-46924-w
View Full Paper
Ask AI
Bookmark
Share