The Impact of Rising Temperatures and Weather Patterns on Grassland Fire Risk: A Machine Learning Approach

Rising temperatures and shifting weather patterns are predicted to significantly impact grassland fire risk. This research investigates the potential of machine learning to improve fire risk prediction in grasslands. We propose a model that utilizes machine learning algorithms to analyse environmental data, including temperature, precipitation, vegetation cover, and historical fire occurrences. By identifying patterns within this data, the model aims to predict areas with a higher risk of grassland fires. This approach could be a valuable tool for land management agencies, enabling them to prioritize preventative measures and resource allocation in high-risk areas. In order to estimate fire danger, the project will investigate the efficacy of different machine learning algorithms and assess the correctness of the model using actual data. The ultimate objective is to help reduce the frequency of grassland fires and enhance fire protection tactics.