Application of the Artificial Neural Network and Support Vector Machines in Forest Fire Prediction in the Guangxi Autonomous Region, China

Li, Yudong; Zhong-ke, Feng; Chen, Shilin; Zhao, Ziyu; Wang, Fengge

doi:10.1155/2020/5612650

Cited by 24 publications

(17 citation statements)

References 27 publications

(28 reference statements)

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“…They are general learning algorithms based on the principle of structural risk minimization. The core idea of an SVM is to establish a classification hyperplane as a decision surface to maximize the isolation edge between the positive and negative examples, thereby providing a high generalization performance [64]. SVMs can improve the ability to transform data from high-dimensional spaces by flexibly using kernel functions when dealing with various nonlinear problems.…”

Section: Support-vector Machinesmentioning

confidence: 99%

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Chinese forest fire occurrence prediction based on machine learning methods

Feng

Zhao

et al. 2021

Preprint

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Forest fires can cause serious harm. Scientifically predicting forest fires is an important basis for preventing them. Currently, there is little research on the prediction of long time-series forest fires in China. Choosing a suitable forest fire prediction model and predicting the probability of Chinese forest fire occurrence are of great importance to China’s forest fire prevention and control work. Based on fire hotspot, meteorological, terrain, vegetation, infrastructure, and socioeconomic data collected from 2003 to 2016, we used a random forest model as a feature-selection method to identify 13 major drivers of forest fires in China. The forest fire prediction models developed in this study are based on four machine-learning algorithms: an artificial neural network, a radial basis function network, a support-vector machine, and a random forest. The models were evaluated using the five performance indicators of accuracy, precision, recall, f1 value, and area under the curve. We used the optimal model to obtain the probability of forest fire occurrence in various provinces in China and created a spatial distribution map of the areas with high incidences of forest fires. The results showed that the prediction accuracy of the four forest fire prediction models was between 75.8% and 89.2%, and the area under the curve value was between 0.840 and 0.960. The random forest model had the highest accuracy (89.2%) and area under the curve value (0.96); thus, it was used as the optimal model to predict the probability of forest fire occurrence in China. 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 can improve forest fire prevention and control by establishing watch towers and using other monitoring equipment. This study helps in understanding the main drivers of forest fires in China, provides a reference for the selection of high-precision forest fire prediction models, and provides a scientific basis for China’s forest fire prevention and control work.

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Section: Support-vector Machinesmentioning

confidence: 99%

“…5. A receiver operating characteristic (ROC) curve is a method used to judge the prediction effect of the model [64]. The prediction accuracy of the model is judged by the value of the AUC, which ranges from 0.5 to 1.…”

Section: Model Performance Evaluationmentioning

confidence: 99%

Chinese forest fire occurrence prediction based on machine learning methods

Feng

Zhao

et al. 2021

Preprint

Self Cite

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show abstract

“…Raw data can reserve all information of the pattern. Inspired by the biological neural networks that constitute animal brains, the artificial neural network (ANN) is applied to do image classification [5], speech separation [6], forest fire prediction [7], etc. However, a major drawback of neural networks is the black-box character.…”

Section: Introductionmentioning

confidence: 99%

An Implicit Memory-Based Method for Supervised Pattern Recognition

Wang

Yang

et al. 2021

Discrete Dynamics in Nature and Society

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How the human brain does recognition is still an open question. No physical or biological experiment can fully reveal this process. Psychological evidence is more about describing phenomena and laws than explaining the physiological processes behind them. The need for interpretability is well recognized. This paper proposes a new method for supervised pattern recognition based on the working pattern of implicit memory. The artificial neural network (ANN) is trained to simulate implicit memory. When an input vector is not in the training set, the ANN can treat the input as a “do not care” term. The ANN may output any value when the input is a “do not care” term since the training process needs to use as few neurons as possible. The trained ANN can be expressed as a function to design a pattern recognition algorithm. Using the Mixed National Institute of Standards and Technology database, the experiments show the efficiency of the pattern recognition method.

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“…Support-vector machines (SVM) are mainly used for pattern classification and nonlinear regression.They are general learning algorithms based on the principle of structural risk minimization. The core idea of SVMs is to establish a classification hyperplane as a decision surface to maximize the isolation edge between the positive and negative examples, thereby providing a high generalization performance[63]. SVMs can improve the ability to transform data from high-dimensional spaces by flexibly using kernel functions when dealing with various nonlinear problems.…”

mentioning

confidence: 99%

“…A ROC (receiver operating characteristic) curve is a method to judge the prediction effect of the model[63]. The prediction accuracy of the model is judged by the value of the area under the curve (AUC).…”

mentioning

confidence: 99%

Research on Multi-Factor Forest Fire Prediction Model Using Machine Learning Method in China

Li¹,

Feng²,

Zhao³

et al. 2020

Preprint

Self Cite

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Forest fires can cause serious harm in many ways. Studying the scientific prediction of forest fires is an important basis for preventing such fires. At present, there is little research on the prediction of long time series forest fires in China. Choosing a suitable forest fire prediction model is of great importance to China’s forest fire prevention and control work. Based on data on fire hotspots, meteorology, terrain, vegetation, infrastructure, and socio-economics collected from 2003 to 2016, we used a random forest model as a feature-selection method to determine 13 major drivers of forest fires in China (such as temperature, terrain etc.). The forest fire prediction models developed in this study are based on four machine-learning algorithms: an artificial neural network, a radial basis function network, a support-vector machine, and a random forest. The models were evaluated using the five performance indicators of accuracy, precision, recall, f1 value, and area-under-the-curve value. We used the optimal model to obtain the probability of forest fire occurrence in various provinces in China and create a spatial distribution map of the areas with high incidences of forest fires. The results show that the prediction accuracy of the four forest fire prediction models is between 75.8% and 89.2%, and the area-under-the-curve value is between 0.840 and 0.960. The random forest model has the highest accuracy (89.2%) and area-under-the-curve value (0.96). It is used as the optimal model to predict the probability of forest fire occurrence in China. The prediction results indicate that the areas with high incidences of forest fires are mainly concentrated in northeastern China (Heilongjiang Province and northern Inner Mongolia Autonomous Region), southeastern China (including Fujian Province and Jiangxi Province) etc. In those areas at high risk of forest fires, the management departments can improve the forest fire prevention and control by establishing watch towers and using other monitoring equipment. This study not only helps in understanding the main drivers of forest fires in China, but it also provides a reference for the selection of high-precision forest fire prediction models and provides a scientific basis for China’s forest fire prevention and control work.

show abstract

Application of the Artificial Neural Network and Support Vector Machines in Forest Fire Prediction in the Guangxi Autonomous Region, China

Cited by 24 publications

References 27 publications

Chinese forest fire occurrence prediction based on machine learning methods

Chinese forest fire occurrence prediction based on machine learning methods

An Implicit Memory-Based Method for Supervised Pattern Recognition

Research on Multi-Factor Forest Fire Prediction Model Using Machine Learning Method in China

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