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FOREST RESOURCES WANAGEMENT ›› 2022, Vol. 0 ›› Issue (2): 109-116.doi: 10.13466/j.cnki.lyzygl.2022.02.015

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Prediction of Shrubbery Fire in Central Yunnan Province Based on the Adaptive NeuroFuzzy Inference System

WEI Jianheng1(), ZHAO Heng1, GAO Zhongliang1(), WANG Hechenyang1, MA Zenan1, WANG Qiuhua1, SHU lifu2, YANG Hongmei3   

  1. 1. Civil Engineering College,Southwest Forestry University,Kunming 650224,China
    2. Institute of Forest Ecological Environment and Protection,Chinese Academy of Forestry,Beijing 100091,China
    3. Shaanxi Academy of Forestry,Yulin,Shaanxi 710082,China
  • Received:2022-01-09 Revised:2022-03-22 Online:2022-04-28 Published:2022-06-13
  • Contact: GAO Zhongliang E-mail:jheng_w@163.com;winalite@swfu.edu.cn

Abstract:

The original vegetation in central Yunnan has been seriously damaged,leading to the continuous growth of flammable shrubbery. With the intensification of global warming,forest fires occur frequently,mainly in shrubs. Therefore,the prediction of forest fires is of great importance to the protection of forest resources in central Yunnan. Based on the data of shrubbery fire and its corresponding meteorological data from 1999 to 2019 in central Yunnan,China.Adaptive Neuro Fuzzy Inference System (ANFIS) and Logistic Regression (LR) were selected,using MATLAB,SPSS 25 and other software to establish a prediction model of forest fire in central Yunnan based on meteorological factors(70% of the data),then the remaining 30% of the data was tested.The results showed that nine meteorological factors were formed into three principal components by principal component analysis. As the input factors of ANFIS model,3 principal components could explain 77.663% of the information of 9 meteorological factors. According to the multicollinearity test,the VIF of the LR model was less than 10,and the precipitation at 24 o'clock,the average 2-minute wind speed,the average daily relative humidity and the daily minimum relative humidity could be obtained,then they were inputted into the model as independent variables of the LR model. According to the screening results of meteorological factors of the two models,the main influencing factors of shrubbery fire in central Yunnan were temperature,wind speed and humidity. By comparing the results of ANFIS and LR model,the accuracy of ANFIS training set was 12% higher than that of LR model,and the accuracy of test set was 10% higher than that of LR model. The ANFIS training set value was 0.96,the test set AUC value was 0.88,and the LR model AUC values were 0.875 and 0.816,respectively. The results showed that the ANFIS model had better adaptability to the model of forest fire in central Yunnan. Results of the research can provide some scientific basis for shrubbery fire prediction in central Yunnan.

Key words: meteorological factors, logistic regression, Adaptive Neuro Fuzzy Inferences System, shrubbery fire, central Yunnan Province

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