Wildfire Risk Assessment in Arid Oasis Ecosystems: An Integrated Machine Learning and Vulnerability Analysis Approach in Morocco.

Climate change is driving an alarming increase in wildfire frequency across arid ecosystems, highlighting the urgent need for more accurate susceptibility mapping to inform prevention efforts. This research evaluates fire risk in the oasis region of Morocco's Middle Ziz Valley through an integrated approach combining GIS technology, satellite remote sensing, and machine learning methods.

A total of 130 fire incidents recorded between 2010 and 2023 were analyzed using NASA's FIRMS database. Nine key factors were considered: topographic variables (slope and aspect), environmental conditions (NDVI, precipitation, temperature, and wind speed), and human influences (land use, road proximity, and distance to residential areas).Four machine learning algorithms were evaluated: Random Forest, Logistic Regression, Support Vector Machine, and XGBoost. Variable importance was determined using Information Gain, while model interpretability was enhanced through SHAP analysis. Ecological health and urban development were further assessed using the Remote Sensing Ecological Index and Night-Time Lights Index, respectively. Integrating these vulnerability measures with fire susceptibility data enabled comprehensive risk mapping across the region.

Random Forest achieved the highest predictive accuracy among the evaluated models. Temperature, wind speed emerged as the primary drivers of fire susceptibility. This adaptable methodological framework provides a robust approach for wildfire risk assessment applicable to other arid ecosystems globally.

Keywords: Wildfire susceptibility, Machine learning, Oasis ecosystems, Vulnerability assessment, Remote sensing, Morocco, Risk mapping.