An analytical review on the most widely used meteorological models in forest fire prediction

Hamadeh, Nizar; Daya, Bassam; Hilal, Alaa; Chauvet, Pierre

doi:10.1109/taeece.2015.7113633

Cited by 18 publications

(6 citation statements)

References 11 publications

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“…This index is one of the simplest ways to assess drought. By analogy with the Nesterov index, five levels of fire hazard are distinguished depending on the index value [79]. The index is calculated by the formula [80]:…”

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confidence: 99%

Forest Fuel Drying, Pyrolysis and Ignition Processes during Forest Fire: A Review

Baranovskiy

Kirienko

2022

Processes

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Forest ecosystems perform several functions that are necessary for maintaining the integrity of the planet’s ecosystem. Forest fires are thus a significant danger to all living things. Forest fire fighting is a foreground task for modern society. Forest fire prediction is one of the most effective ways to solve this urgent issue. Modern prediction systems need to be developed in order to increase the quality of prediction; therefore, it is necessary to generalize knowledge about the processes occurring during a fire. This article discusses the key features of the processes prior to forest fuel ignition (drying and pyrolysis) and the ignition itself, as well as approaches to their experimental and mathematical modeling.

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confidence: 99%

Forest Fuel Drying, Pyrolysis and Ignition Processes during Forest Fire: A Review

Baranovskiy

Kirienko

2022

Processes

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“…Different countries have defined their own fire danger indices considering the pertaining topography and other related factors. For example, the Angstrom index [76] is used in the Scandinavian countries, the Nesterov index is widely used in Russia [77], the Canadian Forest Fire Weather Index (FWI) [45], the United States National Fire Danger Rating System (NFDRS) [46], the McArthur Forest Fire Danger Index (FFDI) is used for open forests in Australia [42], and the McArthur Grassland Fire Danger Rating System is used for grassland areas in Australia [42,78]. These indices provide bushfire danger ratings representing the susceptibility of a particular region to an event of bushfire.…”

Section: Bushfire Predictionmentioning

confidence: 99%

Bushfire Management Strategies: Current Practice, Technological Advancement and Challenges

Bandara,

Navaratnam,

Rajeev

2023

Fire

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Bushfires are classified as catastrophic disasters capable of inflicting significant destruction. The key detrimental consequences of bushfires include the loss of human lives, trauma within communities, economic losses and environmental damage. For example, the estimated economic loss from the September 2019 to March 2020 bushfires in New South Wales (Australia) was about AUD 110 billion, including more than 3000 burned houses. There has been a notable increase in both the frequency and intensity of bushfires, as clearly demonstrated by recent bushfire events. Bushfires are an intricate phenomenon that transpires across various spatial and temporal scales. Further, the changing circumstances of landscapes, vegetation patterns, weather conditions and ecosystems account for the complexity. Therefore, continual attention is essential for the development of bushfire management strategies. In this context, this paper undertakes a comprehensive literature review of bushfire management strategies, encompassing aspects such as bushfire prediction, detection, suppression and prevention. Based on the review, a bushfire management framework is proposed that can eliminate or successfully mitigate the consequences of bushfires. Further, the paper delves into the domains of fire weather conditions, the initiation of bushfires and the adverse consequences stemming from these fires. Both terrestrial and aerial remote sensing methods have proven to be effective in predicting and detecting bushfires. Nevertheless, a simple unique solution cannot be proposed for bushfire management. Changing weather conditions, topography and the geographic mix of asset types need to be considered when deciding on bushfire management strategies and their breadth and depth of application.

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“…Temporal meteorological input includes temperature, rain, humidity, sunshine hours. The choice of these features are informed by indices such as the Angstrom, Nesterov, and Canadian Forest Fire Weather Index as well as the US Fire Danger Rating System [33].…”

Section: Overview Of the Proposed Modelmentioning

confidence: 99%

Data‐driven spatio‐temporal analysis of wildfire risk to power systems operation

Umunnakwe

Parvania

Nguyen

et al. 2022

IET Generation Trans & Dist

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Wildfires are natural or man‐made disasters that continuously threaten portions of the transmission and distribution grid, and thus the stability of the electric grid. This paper presents a two‐stage framework for assessing power system‐wildfire risk using a data‐driven wildfire prediction model. The first stage of the framework estimates the spatio‐temporal probability of potential wildfire ignition and propagation using a deep neural network in combination with the wildfire physical spread model. Analysis reveals similar spatial and temporal patterns between the model‐predicted wildfire ignition potential and actual wildfire ignition. Motivated by these observations, the second stage assesses the wildfire risk in the power grid operation in terms of potential loss of load by de‐energisation, through combining geospatial information system data of the power grid topology and the stochastic spatio‐temporal wildfire model developed in the first stage. The electric power utility applications introduced by the proposed framework are twofold: 1) a spatio‐temporal risk model for proactive de‐energisation against potential power system failure‐induced wildfire, and 2) a spatio‐temporal spreading model for optimal grid operations against exogenous wildfire. The proposed model, based on real‐world dataset, is demonstrated on the IEEE 24‐bus test system mapped to a study area in Northern California, while the results illustrate the proposed model can achieve the best performance in potential wildfire ignition detection (AUC of 0.995) compared to other baselines, as well as demonstrates the risk‐aware operation of the power system enabled by the proposed framework.

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An analytical review on the most widely used meteorological models in forest fire prediction

Cited by 18 publications

References 11 publications

Forest Fuel Drying, Pyrolysis and Ignition Processes during Forest Fire: A Review

Forest Fuel Drying, Pyrolysis and Ignition Processes during Forest Fire: A Review

Bushfire Management Strategies: Current Practice, Technological Advancement and Challenges

Data‐driven spatio‐temporal analysis of wildfire risk to power systems operation

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