Fire Watch: Prediction and Detection of Wildfires Through Advanced AI/ML

Purpose: The proposal aims to address the increasing challenges posed by wildfires, exacerbated by climate change and human settlement patterns. With the rise in wildfire frequency and severity, there's a pressing need for timely and accurate information to aid decision-making during fire mitigation planning and response. Technology: The proposed solution is "FireWatch," an application powered by AI/ML models. This app will predict fire ignition risk, spread, and intensity in near real-time using a combination of remotely sensed data products such as Soil Moisture (SMAP), Global Ecosystem Dynamics Investigation (GEDI), localized weather data (RAWS), and historical fire spread and intensity data (FIRMS & MTBS). Users can input specific coordinates to receive visual displays of expected fire risks, spread direction, speed, and intensity. Additionally, the app will serve the insurance industry by providing validated risk scores to adjust rates based on fire risks across different regions. Intended Use of Funding: - Developing an AI/ML model training dataset using historical fire data. - Aggregating remote sensing datasets. - Correlating vapor pressure data for accurate predictions. - Determining the optimal AI/ML model for wildfire predictions. - Designing an intuitive user interface for the FireWatch application. - Commercialization efforts, including outreach to firefighting agencies, insurance companies, and the general public. Target Markets: The primary beneficiaries of FireWatch will be firefighting entities, insurance companies, and the general public. The application will also serve as a tool for federal and local officials to make informed decisions prior to and during wildfire events.