Spatiotemporal Modelling and Prediction of California Wildfires using Machine Learning and Environmental Data

In 2024, California experienced over eight thousand wildfires in total burning around one million acres. In fact, California experiences the most destructive wildfires in the United States, driven by a complex interplay of environmental, climatic, and human factors. This project aims to develop a predictive framework for identifying high risk wildfire events using historical wildfire datasets. By leveraging machine learning models such as random forest regression, support vector machines, decision tree, gradient descent, etc., the project assesses the likelihood and severity of fire occurrences based on features like, area burned, structures destroyed and damaged. The project attempts to provide visualizations of losses due to these wildfires through an interactive dashboard created using Streamlit in python that enables users to explore spatial and temporal patterns of wildfire susceptibility. Random Forest regression model estimates area burned based on user input data, including latitude, longitude, month, and day, and structures destroyed. The dashboard allows real time predictions alongside dynamic heatmaps showing the density and severity of wildfires. Dimensionality reduction techniques such as Principal Component Analysis (PCA), are applied to uncover essential features that help to improve model efficiency and interpretability. A custom Multifeature Impact Score is created to quantify the impact of each fire by combining key metrics into one easily readable outcome prediction. This enables users to compare the severity of different fires and regions. The dashboard also includes seasonality trend analysis to visualize when wildfires are most common and most destructive, as well as key metric breakdowns by county and year. Together, these features offer a clear, user driven way to reveal patterns in wildfire behavior over time and space. The methodology and tools designed in this work is to raise awareness, support data driven climate discussions, and highlight the increasing threat of wildfires, in alignment with the UN Sustainable Development Goal 13 for Climate Action. The predictive approach presented in this work supports proactive wildfire management, resource allocation, and mitigation planning, in a rapidly changing climate.