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TRV-2026-0677Certified recordPeer-reviewed

Distinguishing compound and cumulative hazards using machine learning and fuzzy logic in multi-hazard susceptibility mapping

This study presents an integrated multi-hazard susceptibility assessment for a mountainous region in northern Iran, focusing on four major hazards: flood, avalanche, rockfall, and landslide. Three machine learning models Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) were applied to model single-hazard susceptibility using 21 topographic, climatic, geological, land-cover, and proximity-related variables at 30 m spatial resolution. Model performance was evaluated using ROC-A…

Climate · G Space — documented gain · certified 2026-08-07 · v1 · article view · machine-readable

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Machine learning models combined with fuzzy logic integration improved prediction of flood, avalanche, rockfall and landslide susceptibility in a mountainous region of northern Iran, with RF and AND operator achieving highest reliability for spatial planning.

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Truvace Impact Record TRV-2026-0677, v1: “Distinguishing compound and cumulative hazards using machine learning and fuzzy logic in multi-hazard susceptibility mapping.” Truvace, 2026-08-07. /record/TRV-2026-0677 (accessed at citation time). sha256 e7de57a38853862e…

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