Suppression resources and their influence on containment of forest fires in Victoria

Abstract

Background Wildfire suppression is becoming more costly and dangerous as the scale and severity of impacts from fires increase under climate change. Aims We aim to identify the key environmental and management variables influencing containment probability for forest fires in Victoria and determine how these change over time. Methods We developed Random Forest models to identify variables driving fire containment within the first 24 h of response. We used a database of ~12 000 incident records collected across Victoria, Australia. Key results Response time, fire size at first attack, number of ground resources deployed (e.g. fire fighters), ignition cause, and environmental factors that influence fire spread (e.g. elevation, humidity, wind, and fuel hazard) were key drivers of suppression success within the first 24 h. However, certainty about the factors influencing suppression reduced as the containment period increased. Conclusions Suppression success hinges on a balance between the environmental factors that drive fire spread and the rapid deployment of sufficient resources to limit fire perimeter growth. Implications Decreasing the period between an ignition and the time of arrival at the fire will allow first responders to begin suppression before the fire size has exceeded their capability to construct a control line.

Smoke and Flame Identification Method for the Entire Process of Grassland Fire Based on YOLOv5m-D and Static and Dynamic Characteristics

“…They proposed a method to identify and detect grassland forest fire smoke by identifying carbon concentration, thereby improving sensor detection accuracy while reducing fire hazards [12]. In view of the high hazard of grassland forest fires, Marshall E and others proposed corresponding identification and detection methods based on the construction of a Random forest model, which effectively improved the probability of curbing grassland forest fires while reducing the actual response time [13]. Cardíl A et al proposed a new innovative method to address the issue of rapid spread of forest and grassland fire activities by clustering relevant hotspots such as visible infrared imaging into fire perimeter timelines, thereby effectively alleviating the actual harm of fires [14].Zhang L et al proposed a multi-scale fusion pyramid network and a fast robust network for early detection of forest fire smoke, which effectively improved detection accuracy [15].…”

“…In formula (12), xy ψ represents the Central moment of order x y + ; ( ) 0 0 , p q represents the actual centroid coordinates of the target area, and its calculation expression is shown in formula ( 13) and ( 14). In formula (13), 0 p represents the abscissa of the center of mass; 10 l represents the first order origin moment; 00 l represents the zero order origin moment. In formula ( 14), 0 q represents the centroid ordinate; 01 l also represents the first order origin moment.…”

Bottlenecks, feedbacks and thresholds in wildfire suppression

“…Predicting the risk of these fires is typically done with fire danger or behaviour indices which can distinguish between days of high potential. Recent work has focused on modelling the probability of initial attack (IA) success (Plucinski 2012;Collins et al 2018;Marshall et al 2022) by different definitions.…”

“…One of the main challenges with these models is that their supporting data can be severely zero-inflated as most IA is successful. Some research has found that response data, such as the number of firefighting resources deployed, is a key predictor of initial attack being unsuccessful (Collins et al 2018;Marshall et al 2022). This highlights the importance of resource allocation and effective management in responding to wildfires.…”