Global Wildfire Outlook Forecast with Neural Networks

Abstract

Wildfire occurrence and spread are affected by atmospheric and land-cover conditions, and therefore meteorological and land-cover parameters can be used in area burned prediction. We apply three forecast methods, a generalized linear model, regression trees, and neural networks (Levenberg–Marquardt backpropagation) to produce monthly wildfire predictions 1 year in advance. The models are trained using the Global Fire Emissions Database version 4 with small fires (GFEDv4s). Continuous 1-year monthly fire predictions from 2011 to 2015 are evaluated with GFEDs data for 10 major fire regions around the globe. The predictions by the neural network method are superior. The 1-year moving predictions have good prediction skills over these regions, especially over the tropics and the southern hemisphere. The temporal refined index of agreement (IOA) between predictions and GFEDv4s regional burned areas are 0.82, 0.82, 0.8, 0.75, and 0.56 for northern and southern Africa, South America, equatorial Asia and Australia, respectively. The spatial refined IOA for 5-year averaged monthly burned area range from 0.69 in low-fire months to 0.86 in high-fire months over South America, 0.3–0.93 over northern Africa, 0.69–0.93 over southern Africa, 0.47–0.85 over equatorial Asia, and 0.53–0.8 over Australia. For fire regions in the northern temperate and boreal regions, the temporal and spatial IOA between predictions and GFEDv4s data in fire seasons are 0.7–0.79 and 0.24–0.83, respectively. The predictions in high-fire months are better than low-fire months. This study illustrates the feasibility of global fire activity outlook forecasts using a neural network model and the method can be applied to quickly assess the potential effects of climate change on wildfires.

Deep Learning Provides Substantial Improvements to County‐Level Fire Weather Forecasting Over the Western United States

“…Statistical methods are versatile in detecting internal features and anomalous patterns regardless of the data domain. Recently, machine learning (ML) methods have led to innovative achievements in various fields (Lecun et al., 2015) and are being actively studied in weather and climate domains (Ham et al., 2019; Kim et al., 2019), including wildfire prediction (Sayad et al., 2019; Song & Wang, 2020). Nevertheless, there is a concern that ML based modeling alone misses the understanding of the dynamical processes governing the occurrence of wildfire and its prediction, and this limitation motivates the present study that uses a hybrid approach combining physics‐based modeling with ML (Pathak et al., 2018; Reichstein et al., 2019).…”

Simulating Forest Fire Spread with Cellular Automation Driven by a LSTM Based Speed Model

“…There are many complex factors influencing the spread of forest fires [4,5]. Accurate simulation [6][7][8][9][10] of forest fire spread can effectively reduce casualties and property losses [11]. The rate of fire spread (ROS) model is one of the important measures for conducting the simulation of forest fire spread, including physical and quasi-physical models and empirical and quasi-empirical models.…”

“…. The subfigures are related to the data slots of No (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15). in Table2, respectively.…”

Forest Fire Prediction with Imbalanced Data Using a Deep Neural Network Method

“…Fuzzy logic models were utilized for forest fire forecasting in [16]. Meteorological and land-cover parameters were used for burned area prediction in [17]. A comprehensive precipitation index was built and used for predicting forest fire risk in central and northern China [18].…”