Forest Fire Occurrence Prediction in China Based on Machine Learning Methods

Forest fires may have devastating consequences for the environment and for human lives. The prediction of forest fires is vital for preventing their occurrence. Currently, there are fewer studies on the prediction of forest fires over longer time scales in China. This is due to the difficulty of for...

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Main Authors: Yongqi Pang, Yudong Li, Zhongke Feng, Zemin Feng, Ziyu Zhao, Shilin Chen, Hanyue Zhang
Format: Article
Language:English
Published: MDPI AG 2022-11-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/14/21/5546
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author Yongqi Pang
Yudong Li
Zhongke Feng
Zemin Feng
Ziyu Zhao
Shilin Chen
Hanyue Zhang
author_facet Yongqi Pang
Yudong Li
Zhongke Feng
Zemin Feng
Ziyu Zhao
Shilin Chen
Hanyue Zhang
author_sort Yongqi Pang
collection DOAJ
description Forest fires may have devastating consequences for the environment and for human lives. The prediction of forest fires is vital for preventing their occurrence. Currently, there are fewer studies on the prediction of forest fires over longer time scales in China. This is due to the difficulty of forecasting forest fires. There are many factors that have an impact on the occurrence of forest fires. The specific contribution of each factor to the occurrence of forest fires is not clear when using conventional analyses. In this study, we leveraged the excellent performance of artificial intelligence algorithms in fusing data from multiple sources (e.g., fire hotspots, meteorological conditions, terrain, vegetation, and socioeconomic data collected from 2003 to 2016). We have tested several algorithms and, finally, four algorithms were selected for formal data processing. There were an artificial neural network, a radial basis function network, a support-vector machine, and a random forest to identify thirteen major drivers of forest fires in China. The models were evaluated using the five performance indicators of accuracy, precision, recall, f1 value, and area under the curve. We obtained the probability of forest fire occurrence in each province of China using the optimal model. Moreover, the spatial distribution of high-to-low forest fire-prone areas was mapped. The results showed that the prediction accuracies of the four forest fire prediction models were between 75.8% and 89.2%, and the area under the curve (AUC) values were between 0.840 and 0.960. The random forest model had the highest accuracy (89.2%) and AUC value (0.96). It was determined as the best performance model in this study. The prediction results indicate that the areas with high incidences of forest fires are mainly concentrated in north-eastern China (Heilongjiang Province and northern Inner Mongolia Autonomous Region) and south-eastern China (including Fujian Province and Jiangxi Province). In areas at high risk of forest fire, management departments should improve forest fire prevention and control by establishing watch towers and using other monitoring equipment. This study helped in understanding the main drivers of forest fires in China over the period between 2003 and 2016, and determined the best performance model. The spatial distribution of high-to-low forest fire-prone areas maps were produced in order to depict the comprehensive views of China’s forest fire risks in each province. They were expected to form a scientific basis for helping the decision-making of China’s forest fire prevention authorities.
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spelling doaj.art-5293ffbb33134db293bba55ed7d759982023-11-24T06:40:47ZengMDPI AGRemote Sensing2072-42922022-11-011421554610.3390/rs14215546Forest Fire Occurrence Prediction in China Based on Machine Learning MethodsYongqi Pang0Yudong Li1Zhongke Feng2Zemin Feng3Ziyu Zhao4Shilin Chen5Hanyue Zhang6Precision Forestry Key Laboratory of Beijing, Beijing Forestry University, Beijing 100083, ChinaPrecision Forestry Key Laboratory of Beijing, Beijing Forestry University, Beijing 100083, ChinaPrecision Forestry Key Laboratory of Beijing, Beijing Forestry University, Beijing 100083, ChinaMinistry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing 100084, ChinaPrecision Forestry Key Laboratory of Beijing, Beijing Forestry University, Beijing 100083, ChinaPrecision Forestry Key Laboratory of Beijing, Beijing Forestry University, Beijing 100083, ChinaPrecision Forestry Key Laboratory of Beijing, Beijing Forestry University, Beijing 100083, ChinaForest fires may have devastating consequences for the environment and for human lives. The prediction of forest fires is vital for preventing their occurrence. Currently, there are fewer studies on the prediction of forest fires over longer time scales in China. This is due to the difficulty of forecasting forest fires. There are many factors that have an impact on the occurrence of forest fires. The specific contribution of each factor to the occurrence of forest fires is not clear when using conventional analyses. In this study, we leveraged the excellent performance of artificial intelligence algorithms in fusing data from multiple sources (e.g., fire hotspots, meteorological conditions, terrain, vegetation, and socioeconomic data collected from 2003 to 2016). We have tested several algorithms and, finally, four algorithms were selected for formal data processing. There were an artificial neural network, a radial basis function network, a support-vector machine, and a random forest to identify thirteen major drivers of forest fires in China. The models were evaluated using the five performance indicators of accuracy, precision, recall, f1 value, and area under the curve. We obtained the probability of forest fire occurrence in each province of China using the optimal model. Moreover, the spatial distribution of high-to-low forest fire-prone areas was mapped. The results showed that the prediction accuracies of the four forest fire prediction models were between 75.8% and 89.2%, and the area under the curve (AUC) values were between 0.840 and 0.960. The random forest model had the highest accuracy (89.2%) and AUC value (0.96). It was determined as the best performance model in this study. The prediction results indicate that the areas with high incidences of forest fires are mainly concentrated in north-eastern China (Heilongjiang Province and northern Inner Mongolia Autonomous Region) and south-eastern China (including Fujian Province and Jiangxi Province). In areas at high risk of forest fire, management departments should improve forest fire prevention and control by establishing watch towers and using other monitoring equipment. This study helped in understanding the main drivers of forest fires in China over the period between 2003 and 2016, and determined the best performance model. The spatial distribution of high-to-low forest fire-prone areas maps were produced in order to depict the comprehensive views of China’s forest fire risks in each province. They were expected to form a scientific basis for helping the decision-making of China’s forest fire prevention authorities.https://www.mdpi.com/2072-4292/14/21/5546forest fire occurrencefeature selectionforest fire driving factorsmachine learningprediction model
spellingShingle Yongqi Pang
Yudong Li
Zhongke Feng
Zemin Feng
Ziyu Zhao
Shilin Chen
Hanyue Zhang
Forest Fire Occurrence Prediction in China Based on Machine Learning Methods
Remote Sensing
forest fire occurrence
feature selection
forest fire driving factors
machine learning
prediction model
title Forest Fire Occurrence Prediction in China Based on Machine Learning Methods
title_full Forest Fire Occurrence Prediction in China Based on Machine Learning Methods
title_fullStr Forest Fire Occurrence Prediction in China Based on Machine Learning Methods
title_full_unstemmed Forest Fire Occurrence Prediction in China Based on Machine Learning Methods
title_short Forest Fire Occurrence Prediction in China Based on Machine Learning Methods
title_sort forest fire occurrence prediction in china based on machine learning methods
topic forest fire occurrence
feature selection
forest fire driving factors
machine learning
prediction model
url https://www.mdpi.com/2072-4292/14/21/5546
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AT yudongli forestfireoccurrencepredictioninchinabasedonmachinelearningmethods
AT zhongkefeng forestfireoccurrencepredictioninchinabasedonmachinelearningmethods
AT zeminfeng forestfireoccurrencepredictioninchinabasedonmachinelearningmethods
AT ziyuzhao forestfireoccurrencepredictioninchinabasedonmachinelearningmethods
AT shilinchen forestfireoccurrencepredictioninchinabasedonmachinelearningmethods
AT hanyuezhang forestfireoccurrencepredictioninchinabasedonmachinelearningmethods