Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.
Department of Civil Engineering, Kermanshah University of Technology, Kermanshah, Iran.
J Environ Manage. 2020 Apr 15;260:109867. doi: 10.1016/j.jenvman.2019.109867. Epub 2020 Jan 22.
Forests are important dynamic systems which are widely affected by fire worldwide. Due to the complexity and non-linearity of the forest fire problem, employing hybrid evolutionary algorithms is a logical task to achieve a reliable approximation of this environmental threat. Three fuzzy-metaheuristic ensembles, based on adaptive neuro-fuzzy inference systems (ANFIS) incorporated with genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE) evolutionary algorithms are used to produce the forest fire susceptibility map (FFSM) of a fire-prone region in Iran. A sensitivity analysis is also executed to evaluate the effectiveness of the proposed ensembles in terms of time and complexity. The results revealed that all models produce FFSMs with acceptable accuracy. However, the superiority of the GA-ANFIS was shown in both recognizing the pattern (AUROC = 0.912 and Error = 0.1277) and predicting unseen fire events (AUROC = 0.850 and Error = 0.1638). The optimized structures of the proposed GA-ANFIS and PSO-ANFIS ensembles could be good alternatives to traditional forest fire predictive models, and their FFSMs can be promisingly used for future planning and decision making in the proposed area.
森林是受到全球范围内广泛影响的重要动态系统。由于森林火灾问题的复杂性和非线性，采用混合进化算法是实现对这种环境威胁可靠逼近的合理任务。本研究使用三种基于自适应神经模糊推理系统（ANFIS）与遗传算法（GA）、粒子群优化（PSO）和差分进化（DE）进化算法相结合的模糊元启发式集成，来生成伊朗一个易发生火灾地区的森林火灾易感性图（FFSM）。此外，还进行了敏感性分析，以评估所提出的集成在时间和复杂性方面的有效性。结果表明，所有模型生成的 FFSM 都具有可接受的准确性。然而，GA-ANFIS 的优越性体现在识别模式（AUROC = 0.912，Error = 0.1277）和预测未见火灾事件（AUROC = 0.850，Error = 0.1638）方面。所提出的 GA-ANFIS 和 PSO-ANFIS 集成的优化结构可以作为传统森林火灾预测模型的替代方案，其 FFSM 可以为拟议地区的未来规划和决策提供有希望的依据。
