A Genetic Algorithm for Forest Firefighting Optimization

Matos, Marina A.; Rocha, Ana Maria A. C.; Costa, Lino; Alvelos, Filipe Pereira e

doi:10.1007/978-3-031-10562-3_5

Cited by 4 publications

(1 citation statement)

References 25 publications

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“…Lack of prediction of fire intensity may lead to uneven resource allocation and waste of limited firefighting resources. If we can accurately predict the intensity of fire combustion and combine it with the area of fire spread, we can develop more reasonable resource allocation strategies to concentrate limited resources in the areas where the fire is most intense, in order to maximize the outcome of firefighting [32]. For example, we can prioritize aerial firefighting efforts in areas with higher fire intensity to maximize the impact of limited resources [33]- [35].…”

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confidence: 99%

A Deep Learning Framework: Predicting Fire Radiative Power From the Combination of Polar-Orbiting and Geostationary Satellite Data During Wildfire Spread

Dong,

Zheng,

Zhao

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Fire Radiative Power (FRP) is a key indicator for evaluating the intensity of wildfires, unlike traditional realtime fire lines or combustion areas that only provide binary information, and its accurate prediction is more important for firefighting actions and environmental pollution assessment. To this end, we used a combination of data from geostationary satellites and polar orbit satellites to correct FRP data. Incorporating various factors that affect wildfire spread, such as meteorological conditions, topography, vegetation indexes, and population density, we constructed a comprehensive California wildfire spread dataset, covering information since 2017. Then, we established a deep learning framework that integrates various modules to analyze multimodal data for the accurate prediction of FRP imagery. We investigated the impact of input sequence length and loss function design on model predictive performance, leading to subsequent model optimization. Furthermore, our model has demonstrated acceptable performance in transfer learning and multi-step prediction, emphasizing its application value in wildfire prediction and management. It can provide more detailed information about wildfire spread, showcasing the powerful capability of deep learning to process multimodal data and its potential in the emerging field of real-time FRP prediction.

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confidence: 99%