RAFFIA: Short-term Forest Fire Danger Rating Prediction via Multiclass Logistic Regression

Lei Wang, Qingjian Zhao, Zuomin Wen and Jiaming Qu
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Lei Wang: School of Economics and Management, and Intelligent Big Service Laboratory (InBSLab), Nanjing Forestry University, Nanjing 210037, China
Qingjian Zhao: School of Economics and Management, and Intelligent Big Service Laboratory (InBSLab), Nanjing Forestry University, Nanjing 210037, China
Zuomin Wen: School of Economics and Management, and Intelligent Big Service Laboratory (InBSLab), Nanjing Forestry University, Nanjing 210037, China
Jiaming Qu: School of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3175, USA

Sustainability, 2018, vol. 10, issue 12, 1-16

Abstract: Forest fire prevention is important because of human communities near forests or in the wildland-urban interfaces. Short-term forest fire danger rating prediction is an effective way to provide early guidance for forest fire managers. It can therefore effectively protect the forest resources and enhance the sustainability of the forest ecosystem. However, relevant existing forest fire danger rating prediction models operate well only when applied to distinct climates and fuel types separately. There are desires for an effective methodology, which can construct a specific short-term prediction model according to an evaluation of the data from that specific region. Moreover, a suitable method for prediction model construction needs to deal with some big data related computing challenges (i.e., data diversity coupled with complexity of solution space, and the requirement of real-time forest fire prevention application) when massively observed heterogeneous parameters are available for prediction (e.g., meteorology factor, the amount of litter in the area, soil moisture, etc.). To capture the influences of multiple prediction factors on the prediction results and effectively learn from fast cumulative historical big data, artificial intelligence methods are investigated in this paper, yielding a short-term Ratings of Forest Fire Danger Prediction via Multiclass Logistic Regression (or RAFFIA) model for forest fire danger rating online prediction. Experimental evaluations conducted on a sensor-based forest fire prevention experimental station show that RAFFIA (with 98.71% precision and 0.081 root mean square error) is more effective than the Least Square Fitting Regression (LSFR) and Random Forests (RF) prediction models.

Keywords: forest fire; danger rating online prediction; multiclass logistic regression; artificial intelligence; big data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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