Predicting Risks of Forest Fires using Federated Machine Learning Methods

Globally, the livelihoods of hundreds of millions of people directly depend upon their local forest ecosystems. However, according to data from the Canadian National Forestry Database, over 8,500 forest fires occurred each year between 1980-2020, burning more than 2 million hectares every year. Forest fires triggered insured losses of almost CAD 5 billion between 2003-2017. Therefore, it is important to develop efficient, integrated forest fire management (IFFM) systems to reduce the losses. One of the most important components of an IFFM system is the forecasting of forest fire danger conditions (FFDC), namely, detecting fires and predicting their spread. In general, FFDCs are highly dependent on meteorological variables (MV), biophysical variables (BV), and topography (TG) of forests, and accurately predicting FFDCs becomes a complex task. Existing FFDC prediction methodologies use only one or two kinds of variables, leading to less accurate predictions. The researchers and industry partners will design a software framework for predicting FFDCs using all the three kinds of data, namely, MV, BV, and TG, for better accuracy. We will apply machine/deep learning methods to predict FFDCs because of the complex interplay among the three kinds of data in igniting and spreading forest fires. Our framework will result in better prediction accuracy because it considers all the three types of data and multiple optimized models. In addition, federated machine learning methods will accelerate the prediction process, giving firefighters extra valuable time to manage fires. We will validate the system by using publicly available data for Ontario and Alberta. The expertise gained from the proposed system will expand the portfolios of the two partners who will offer the research outcomes as new, expanded service offerings to their clients -- public and private sector companies who focus on fighting forest fires in Canada. The project will have a tremendous impact on both the economy and society of Canada, and the research can also be leveraged for the study of floods and climate change. HQP trained as part of the program will fill roles in the growing sectors of natural resource management and climate change.