Predicting Risks of Forest Fires using Federated Machine Learning Methods

使用联合机器学习方法预测森林火灾风险

• 批准号: 570503-2021
• 负责人: Naik, Kshirasagar
• 金额: $ 12.81万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=722713
• 关键词: Predicting; Risks; Forest; Fires; using

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.

在全球范围内，数亿人的生计直接依赖于当地的森林生态系统。然而，根据加拿大国家林业数据库的数据，1980年至2020年期间，每年发生8,500多起森林火灾，每年烧毁200多万公顷土地。2003年至2017年间，森林火灾引发了近50亿加元的保险损失。因此，发展高效、综合的森林火灾管理系统以减少损失是非常重要的。IFFM系统最重要的组成部分之一是预测森林火险状况，即探测火灾并预测其蔓延。一般来说，FFDC高度依赖于气象变量（MV），生物物理变量（BV）和地形（TG）的森林，准确预测FFDC成为一项复杂的任务。现有的FFDC预测方法仅使用一种或两种变量，导致预测不太准确。研究人员和行业合作伙伴将设计一个软件框架，用于使用所有三种数据（即MV，BV和TG）预测FFDC，以提高准确性。我们将应用机器/深度学习方法来预测FFDC，因为这三种数据在点燃和蔓延森林火灾中存在复杂的相互作用。我们的框架将导致更好的预测准确性，因为它考虑了所有三种类型的数据和多个优化模型。此外，联邦机器学习方法将加速预测过程，为消防员提供额外的宝贵时间来管理火灾。我们将通过使用安大略和阿尔伯塔的公开数据来验证该系统。从拟议的系统中获得的专门知识将扩大这两个伙伴的组合，它们将把研究成果作为新的、扩大的服务提供给它们的客户-在加拿大重点扑灭森林火灾的公共和私营部门公司。该项目将对加拿大的经济和社会产生巨大影响，研究也可以用于洪水和气候变化的研究。作为该计划的一部分，HQP将在自然资源管理和气候变化等日益增长的部门中发挥作用。