A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area

• Published in: Agricultural and forest meteorology 2017-02, Vol.233, p.32-44
• Main Authors: Tien Bui, Dieu, Bui, Quang-Thanh, Nguyen, Quoc-Phi, Pradhan, Biswajeet, Nampak, Haleh, Trinh, Phan Trong
• Format: Article
• Language: English
• Subjects: Forest fire; GIS; Lam Dong; Neural-fuzzy; Particle swarm optimization; Vietnam
• ISSN: 0168-1923; 1873-2240
• DOI: 10.1016/j.agrformet.2016.11.002

•PSO-NF is proposed for forest fire modeling.•PSO-NF has high performance on the training and the validation datasets.•PSO-NF outperforms benchmark models, RF and SVM. This paper proposes and validates a novel hybrid artificial intelligent approach, named as Particle Swarm Optimized Neural Fuzzy (PSO-NF), for spatial modeling of tropical forest fire susceptibility. In the proposed approach, a Neural Fuzzy inference system (NF) was used to establish the forest fire model whereas Particle Swarm Optimization (PSO) was adopted to investigate the best values for the model parameters. Tropical forest at the province of Lam Dong (Central Highland of Vietnam) was used as a case study. For this purpose, historic forest fires and ten ignition factors (slope, aspect, elevation, land use, Normalized Difference Vegetation Index, distance to road, distance to residence area, temperature, wind speed, and rainfall) were collected from various sources to construct a GIS database, and then, the database was used to develop and validate the proposed model. The performance of the forest model was assessed using the Receiver Operating Characteristic curve, area under the curve (AUC), and several statistical measures. The results showed that the proposed model performs well, both on the training dataset (AUC=0.932) and the validation dataset (AUC=0.916). The usability of the proposed model was further assessed through comparisons with those derived from two benchmark state-of-the art machine learning methods, Random Forests (RF) and Support Vector Machine (SVM). Because the performance of the proposed model is better than the two benchmark models, we concluded that the PSO-NF model is a valid alternative tool that should be considered for tropical forest fire susceptibility modeling. The result in this study is useful for forest planning and management in forest fire prone areas.