Mapping forest fire susceptibility using spatially explicit ensemble models based on the locally weighted learning algorithm

• Published in: Ecological informatics Vol. 63; p. 101292
• Main Authors: Tuyen, Tran Thi, Jaafari, Abolfazl, Yen, Hoang Phan Hai, Nguyen-Thoi, Trung, Phong, Tran Van, Nguyen, Huu Duy, Van Le, Hiep, Phuong, Tran Thi Mai, Nguyen, Son Hoang, Prakash, Indra, Pham, Binh Thai
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2021
• Subjects: Bagging; Cascade Generalization; Dagging; Decorate; Ensemble modeling; Machine learning; Decorate; Dagging; Bagging; Machine learning; Ensemble modeling; Cascade Generalization
• ISSN: 1574-9541
• DOI: 10.1016/j.ecoinf.2021.101292

Fire is among the most dangerous and devastating natural hazards in forest ecosystems around the world. The development of computational ensemble models for improving the predictive accuracy of forest fire susceptibilities could save time and cost in firefighting efforts. Here, we combined a locally weighted learning (LWL) algorithm with the Cascade Generalization (CG), Bagging, Decorate, and Dagging ensemble learning techniques for the prediction of forest fire susceptibility in the Pu Mat National Park, Nghe An Province, Vietnam. A geospatial database that contained records from 56 historical fires and nine explanatory variables was employed to train the standalone LWL model and its derived ensemble models. The models were validated for their goodness-of-fit and predictive capability using the area under the receiver operating characteristic curve (AUC) and several other statistical performance criteria. The CG-LWL and Bagging-LWL models with AUC = 0.993 showed the highest training performance, whereas the Dagging-LWL ensemble model with AUC = 0.983 performed better than Decorate-LWL (AUC = 0.976), CG-LWL and Bagging-LWL (AUC = 0.972), and LWL (AUC = 0.965) for predicting the spatial pattern of fire susceptibilities across the study area. Our study promotes the application of ensemble models in forest fire prediction and enhances the researchers' understanding of the processes of model building. Although these four ensemble models were originally developed for the estimation of forest fire susceptibility, the models are sufficiently general to be used for predicting other types of natural hazards, such as landslides, floods, and dust storms, by considering local geo-environmental factors. •Developing four ensemble models for forest fire susceptibility mapping•Testing performance of CG, Bagging, Decorate, and Dagging ensemble learners•Reliable susceptibility mapping using the Dagging-LWL model (AUC = 0.983)•Providing insights for developing more advanced forest fire predictive models