Application of a stochastic compartmental model to approach the spread of environmental events with climatic bias

Joan Boters Pitarch; María Signes-Pont; Julian Szymański; Higinio Mora-Mora

doi:10.1016/j.ecoinf.2023.102266

Application of a stochastic compartmental model to approach the spread of environmental events with climatic bias

Abstrakt

Wildfires have significant impacts on both environment and economy, so understanding their behaviour is crucial for the planning and allocation of firefighting resources. Since forest fire management is of great concern, there has been an increasing demand for computationally efficient and accurate prediction models. In order to address this challenge, this work proposes applying a parameterised stochastic model to study the propagation of environmental events, focusing on the bias introduced by climatic variables such as wind. This model’s propagation occurs in a grid where cells are classified into different compartments based on their state. Furthermore, this approach generalises previous non-stochastic models, which are now considered particular cases within this broader framework. The use of the Monte Carlo method is highlighted, which allows for obtaining probabilistic estimates of the state of the cells in each time step, considering a level of confidence. In this way, the model provides a tool to obtain a quantitative estimate of the probability associated with each state in the spread of forest fires.

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Wersja publikacji: Accepted albo Published Version
DOI:: Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1016/j.ecoinf.2023.102266
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Słowa kluczowe

STOCHASTIC MODELLING

Informacje szczegółowe

Kategoria:

Publikacja w czasopiśmie

Typ:

artykuły w czasopismach

Opublikowano w:

Ecological Informatics nr 77,
ISSN: 1574-9541

Język:

angielski

Rok wydania:

2023

Opis bibliograficzny:

Boters Pitarch J., Signes-Pont M., Szymański J., Mora-Mora H.: Application of a stochastic compartmental model to approach the spread of environmental events with climatic bias// Ecological Informatics -Vol. 77, (2023), s.102266-

DOI: