Modeling the Susceptibility of Forest Fires Using a Genetic Algorithm: A Case Study in Mountain Areas of Southwestern China

Zheng, Zhong; Gao, Yanghua; Zhang, Ju; Chen, Zhijun

doi:10.1155/2022/5502209

Cited by 2 publications

(1 citation statement)

References 46 publications

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“…Especially in China, an average of about 1900 forest fires occurred per year during the period 2017-2021, leading to the destruction of around 13,000 hectares of forest. Therefore, researchers have made significant efforts in the field concerning forest fires, including research on forest fire prediction [4,5], forest fire spread [6,7], and burn severity of forest fires [8,9]. This study aimed to carry out research related to forest fire prediction in order to mitigate the ecological losses caused by fires.…”

Section: Introductionmentioning

confidence: 99%

A PSO-CNN-Based Deep Learning Model for Predicting Forest Fire Risk on a National Scale

You,

Zheng,

Yang

et al. 2023

Forests

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Forest fires have a significant impact on terrestrial ecosystems, leading to harm to biodiversity and environment. To mitigate the ecological damage caused by forest fires, it was necessary to develop prediction models of fire risk. In this study, by evolving the optimal architecture and parameters using the particle swarm optimization (PSO) algorithm, a convolutional neural network (CNN) deep learning model was proposed to predict forest fire risk on a national scale. Utilizing fire data and fire risk factors from 2001 to 2020 in China, the PSO-CNN-based deep learning model (PSO-CNN) was utilized and tested. Compared to logistic regression, random forest, support vector machine, k-nearest neighbors, and CNN models, the PSO-CNN model exhibited superior performance with an accuracy of 82.2% and an AUC value of 0.92. These results clearly highlighted the effectiveness of the PSO-CNN model in enhancing the accuracy of forest fire prediction. Furthermore, the forest fire risk prediction level estimated by the proposed model on a national scale for the entire country was mostly consistent with actual fire data distribution, indicating its potential to be used as an important direction for deep learning in forest fire prediction research.

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Section: Introductionmentioning

confidence: 99%

A PSO-CNN-Based Deep Learning Model for Predicting Forest Fire Risk on a National Scale

You,

Zheng,

Yang

et al. 2023

Forests

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Trending and emerging prospects of physics-based and ML-based wildfire spread models: a comprehensive review

Singh,

Ang,

Lewis

et al. 2024

J. For. Res.

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The significant threat of wildfires to forest ecology and biodiversity, particularly in tropical and subtropical regions, underscores the necessity for advanced predictive models amidst shifting climate patterns. There is a need to evaluate and enhance wildfire prediction methods, focusing on their application during extended periods of intense heat and drought. This study reviews various wildfire modelling approaches, including traditional physical, semi-empirical, numerical, and emerging machine learning (ML)-based models. We critically assess these models’ capabilities in predicting fire susceptibility and post-ignition spread, highlighting their strengths and limitations. Our findings indicate that while traditional models provide foundational insights, they often fall short in dynamically estimating parameters and predicting ignition events. Cellular automata models, despite their potential, face challenges in data integration and computational demands. Conversely, ML models demonstrate superior efficiency and accuracy by leveraging diverse datasets, though they encounter interpretability issues. This review recommends hybrid modelling approaches that integrate multiple methods to harness their combined strengths. By incorporating data assimilation techniques with dynamic forecasting models, the predictive capabilities of ML-based predictions can be significantly enhanced. This review underscores the necessity for continued refinement of these models to ensure their reliability in real-world applications, ultimately contributing to more effective wildfire mitigation and management strategies. Future research should focus on improving hybrid models and exploring new data integration methods to advance predictive capabilities.

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Modeling the Susceptibility of Forest Fires Using a Genetic Algorithm: A Case Study in Mountain Areas of Southwestern China

Cited by 2 publications

References 46 publications

A PSO-CNN-Based Deep Learning Model for Predicting Forest Fire Risk on a National Scale

A PSO-CNN-Based Deep Learning Model for Predicting Forest Fire Risk on a National Scale

Trending and emerging prospects of physics-based and ML-based wildfire spread models: a comprehensive review

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