Predicting Wildfires with Machine Learning: Insights from Pyrocumulonimbus Clouds Dynamics

This research presents a full-stack, machine learning-powered platform for real-time wildfire forecasting, specifically targeting Pyrocumulonimbus (pyroCb) cloud formations—intense storm systems triggered by wildfires and increasingly common due to climate change. These clouds worsen wildfire behavior through dry lightning and erratic winds, making early detection critical for disaster response. The system integrates three core models: a CNN for identifying pyroCb patterns from satellite imagery, XGBoost for predicting wildfire risk based on environmental factors (e.g., temperature, humidity, wind speed), and an SVM to refine classification in complex atmospheric conditions. These models are fused in an ensemble to boost accuracy and interpretability. A notable innovation is client-side inference via TensorFlow.js and WebGL, enabling model execution within the browser, removing the need for cloud GPU resources and making it usable in low-bandwidth, remote areas. Supabase serves as the backend, using PostgreSQL for secure and spatially indexed data storage. The interface, built with React, Leaflet.js, and Chart.js, offers geospatial mapping, interactive cloud analysis, and real-time dashboards in a responsive, mobile-friendly design. This showcases how AI and web technologies can power scalable, low-latency, field-ready solutions for environmental monitoring and wildfire risk mitigation. Have a look on the development and try with cloud images if this fascinates you: PyroScan