Mapping China’s Forest Fire Risks with Machine Learning
TL;DR: In this article , a large-scale forest fire risk map for China was constructed based on Visible Infrared Imaging Radiometer Suite data from 17,330 active fires for the period 2012-2019, and combined terrain, meteorology, social economy, vegetation, and other factors closely related to the generation of forest fire disasters for modeling and predicting forest fires.
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Abstract: Forest fires are disasters that are common around the world. They pose an ongoing challenge in scientific and forest management. Predicting forest fires improves the levels of forest-fire prevention and risk avoidance. This study aimed to construct a forest risk map for China. We base our map on Visible Infrared Imaging Radiometer Suite data from 17,330 active fires for the period 2012–2019, and combined terrain, meteorology, social economy, vegetation, and other factors closely related to the generation of forest-fire disasters for modeling and predicting forest fires. Four machine learning models for predicting forest fires were compared (i.e., random forest (RF), support vector machine (SVM), multi-layer perceptron (MLP), and gradient-boosting decision tree (GBDT) algorithm), and the RF model was chosen (its accuracy, precision, recall, F1, AUC values were 87.99%, 85.94%, 91.51%, 88.64% and 95.11% respectively). The Chinese seasonal fire zoning map was drawn with the municipal administrative unit as the spatial scale for the first time. The results show evident seasonal and regional differences in the Chinese forest-fire risks; forest-fire risks are relativity high in the spring and winter, but low in fall and summer, and the areas with high regional fire risk are mainly in the provinces of Yunnan (including the cities of Qujing, Lijiang, and Yuxi), Guangdong (including the cities of Shaoguan, Huizhou, and Qingyuan), and Fujian (including the cities of Nanping and Sanming). The major contributions of this study are to (i) provide a framework for large-scale forest-fire risk prediction having a low cost, high precision, and ease of operation, and (ii) improve the understanding of forest-fire risks in China.
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Assessment of China’s forest fire occurrence with deep learning, geographic information and multisource data
TL;DR: A novel framework to assess forest fire risks and policy decisions on forest fire management in China is developed using deep learning algorithms, geographic information, and multisource data that vividly integrates various factors from the environment and human activities.
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Mapping Forest Fire Risk Zones Using Machine Learning Algorithms in Hunan Province, China
TL;DR: Zhang et al. as discussed by the authors selected 19 forest fire impact factors based on satellite monitoring hotspot data, meteorological data, topographic data, vegetation data, and social and human data from 2010-2018.
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TL;DR: In this paper , the authors used the H2O driverless artificial intelligence (DAI) cloud platform to model forest fire probability in the Lower Silesian Voivodeship of Poland.
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