A Data-Driven Model for Wildfire Prediction in California

Wildfires in California have consistently been a concern that has been getting more acute in the past years. Rampant wildfires have consistently hit the state of California, creating severe economic and environmental loss. In 2023, wild-fires cost nearly 1.2 billion U.S. dollars in financial loss between January and September. Between 2021–2022, wildfires accounted for over 11.2 billion in damage across the United States. Over 1.6 million acres of land have burned and caused large sums of environmental damage. The increasing frequency and severity of wildfires in California have led to a growing need for accurate and reliable wildfire risk assessments. In this research, we propose a machine learning approach based on six different classifiers to determine wildfire risk using environmental data from California. We use a dataset of historical wildfire occurrences and various environmental variables such as temperature, humidity, and wind speed to build a recommendation model using a Random Forest classifier. We use the SMOTE technique to handle class imbalance.