Contribution of remote sensing to wildfire trend and dynamic analysis in two of Ghana’s ecological zones: Guinea-savanna and Forest-savanna mosaic

Dahan, Kueshi Sémanou; Kasei, Raymond Abudu; Husseini, Rikiatu

doi:10.21203/rs.3.rs-2350927/v1

Cited by 1 publication

(1 citation statement)

References 73 publications

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“…Climate variables also play a crucial role in the occurrence of forest fires [47,48]. In this study, the variables of average temperature, average relative humidity, precipitation, and average wind speed were used.…”

Section: Fire Risk Factorsmentioning

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: Fire Risk Factorsmentioning

confidence: 99%