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Abstract

As the world is increasing focus on climate change issues and mitigation strategies, wildfires are emerging as a frequent source of GHG emission. (E.g. Amazon fires in summer 2019 or Australian bush fires in early 2020). Such events are playing a major role in accelerating the crisis in hand. The project aims to create a wildfire forecasting model based on geological and demographic datasets and quantify its impact on the environment and lives of people based on loss of biomass and proportional increase in respiratory illness in target population.

Different classification algorithms such as neural networks, KNN, SVM, naive byes were used to find the appropriate accuracy of wildfire forecasting model. Also SARIMAX was used for time series analysis to identify the spontaneous combustion temperature. Further this project aims to analyse the driving factors of forest fires and recommend disaster mitigation strategies