Advanced Wildfire Prediction in Morocco: Developing a Deep Learning Dataset From Multisource Observations - Ayoub Jadouli; Chaker El El Amrani (IEEE Access)
This study introduces a novel dataset for wildfire prediction in Morocco, integrating multisource observations to address the country’s unique geographical and climatic challenges. We compile essential environmental indicators and employ state-of-the-art machine learning (ML) and deep learning (DL) algorithms to predict next-day wildfire occurrences. Our best-performing models achieve an accuracy of up to 90%, significantly improving upon traditional approaches. The key contributions include: 1) A localized dataset tailored to Morocco’s conditions; 2) benchmarking of advanced ML and DL algorithms; and 3) open sharing of the dataset and codebase. We discuss the potential applicability of our methodology to other regions and highlight future research directions. This work advances dataset creation techniques and emphasizes the importance of localized research for effective wildfire management strategies in underrepresented areas.