CMCC Talks
13 January 2026, 12:00 CET
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Speaker: Shahbaz Alvi, CMCC
Moderator: Soheil Shayegh, CMCC
Machine learning method leverage the growing availability of large volumes of precision data collected through cutting-edge remote sensing and in-situ technology. In wildfire risk assessment, ML methods capitalize high precision data, and exploit variables that are not easily integrable in the canonical process-based frameworks. On the other hand, on an operational level, traditional methods largely outweigh the use of ML models. This talk aims to assess the use of ML methods in wildfire risk assessment and discusses metrics for comparing ML models performance. We further discuss the operational applicability of ML models in fire danger assessment.