Predicting forest fire risk based on mining rules with ant-miner algorithm in cloud-rich areas
Zhong Zheng, Yanghua Gao, Qingyuan Yang, Bin Zou, Yongjin Xu, Yanying Chen, Shiqi Yang, Yongqian Wang, Zengwu Wang
Abstract
Annually, millions of hectares of forest lands around the world are destroyed by fires. To minimize the fire-caused losses, more studies on the risk prediction of forest fires need to be carried out. For predicting the risk of forest fires in cloud-rich areas (e.g., the southwest of China), the synergetic use of operational forecasting systems and remote sensing-based models is expected to have a consistent performance. Therefore, we proposed in this study a new model based on ant-miner algorithm which has a good capability of solving multivariable and non-linear problems in the synergetic modeling of multi-source data. Based on historical fire data during 2000–2018 in Chongqing city, its performance was tested, and then was compared with that of other three models (i.e., meteorological data-, Artificial Neural Network-, and Support Vector Machine-based models). Results showed that, without interference from human factors, the risk predictions of proposed model were more objective. And, its mined-rules were easier to understand and also portable across multiple GIS platforms. Moreover, the proposed model has a better performance at predicting risk levels (i.e., overall accuracy was 79.02% and Kappa coefficient was 0.678) and the spatial distribution of its predictions were more detailed. This research indicated that the ant-miner algorithm-based model was more effective and reliable, and it could be used for constructing the operational system of risk predictions for forest fires in cloud-rich areas.