The development and implementation of a human-caused wildland fire occurrence prediction system for the province of Ontario, Canada

Abstract

We describe the development and implementation of an operational human-caused wildland fire occurrence prediction (FOP) system in the Province of Ontario, Canada. A suite of supervised statistical learning models was developed using more than 50 years of high-resolution data over a 73.8 million hectare study area, partitioned into Ontario’s Northwest and Northeast Fire Management Regions. A stratified modelling approach accounts for different seasonal baselines regionally and for a set of communities in the far north. Response-dependent sampling and modelling techniques using logistic Generalized Additive Models are used to develop a fine-scale, spatio-temporal FOP system with models that include non-linear relationships with key predictors. These predictors include inter and intra-annual temporal trends, spatial trends, ecological variables, fuel moisture measures, human land use characteristics and a novel measure of human activity. The system produces fine-scale, spatially explicit maps of daily probabilistic human-caused FOP based on locally observed conditions along with point and interval predictions for the expected number of fires in each region. A simulation-based approach for generating the prediction intervals is described. Daily predictions were made available to fire management practitioners through a custom dashboard and integrated into daily regional planning to support detection and fire suppression preparedness needs.

Comparing calibrated statistical and machine learning methods for wildland fire occurrence prediction: a case study of human-caused fires in Lac La Biche, Alberta, Canada

“…There are several advantages to developing FOP models that predict true probabilities. As discussed and illustrated in Woolford et al (2021), such predictions can be summed to predict the expected number of fires in a given region (such as a region or sector used by fire management) on a given day; they can be used to create spatially explicit colour-coded fire occurrence maps that are interpretable; and they can be used to produce prediction intervals that reflect the uncertainty in such predictions. Spatially explicit FOP output can also be incorporated into risk-based frameworks to aid aerial detection routeing (e.g.…”

“…There are many different methods that have been used to model wildland fire occurrences. Recent summaries, reviews and discussions appear in Plucinski (2012), Taylor et al (2013), Costafreda-Aumedes et al (2017), Nadeem et al (2020) and Woolford et al (2021). These methods can be broadly viewed as coming from one of the following two dominant data modelling cultures: model-based (i.e.…”

“…Non-linear relationships between the response and covariates are commonly modelled using spline-smoothers in logistic GAMs (e.g. Brillinger et al 2003;Preisler et al 2004;Vilar et al 2010;Woolford et al 2011Woolford et al , 2021Magnussen and Taylor 2012). Automatic variable selection methods are based on regularisation approaches, such as lasso LR (Tibshirani 1996), that modify the likelihood function used to fit the models so that coefficients for non-important predictors are 'shrunk' towards zero (e.g.…”

GIS-Based Forest Fire Risk Model: A Case Study in Laoshan National Forest Park, Nanjing

“…Forests are not only important strategic resources for social development, but also have great influence on the protection of species diversity [1]. Climate and human activities are important factors that can cause forest fires, such as temperature, precipitation, sacrifice [2,3]. Combustibles are also significant for fires, which are trees and turf in the forest.…”

Considerations for Categorizing and Visualizing Numerical Information: A Case Study of Fire Occurrence Prediction Models in the Province of Ontario, Canada

“…FOP is one of the pillars of situational awareness, a requirement for daily and multiday preparedness planning [1] and a key component for modelling risk [2]. Ontario's fire management agency has a lightning-caused FOP model [3] and, more recently, a humancaused FOP model [1] that was developed in collaboration between the agency's science specialists and decision-makers and external researchers.…”