Climate Change Data Portal
DOI | 10.1016/j.jenvman.2019.04.117 |
Genetic and firefly metaheuristic algorithms for an optimized neuro-fuzzy prediction modeling of wildfire probability | |
Jaafari, Abolfazl1; Termeh, Seyed Vahid Razavi2; Dieu Tien Bui3 | |
发表日期 | 2019 |
ISSN | 0301-4797 |
EISSN | 1095-8630 |
卷号 | 243页码:358-369 |
英文摘要 | In the terrestrial ecosystems, perennial challenges of increased frequency and intensity of wildfires are exacerbated by climate change and unplanned human activities. Development of robust management and suppression plans requires accurate estimates of future burn probabilities. This study describes the development and validation of two hybrid intelligence predictive models that rely on an adaptive neuro-fuzzy inference system (ANFIS) and two metaheuristic optimization algorithms, i.e., genetic algorithm (GA) and firefly algorithm (FA), for the spatially explicit prediction of wildfire probabilities. A suite of ten explanatory variables (altitude, slope, aspect, land use, rainfall, soil order, temperature, wind effect, and distance to roads and human settlements) was investigated and a spatial database constructed using 32 fire events from the Zagros ecoregion (Iran). The frequency ratio model was used to assign weights to each class of variables that depended on the strength of the spatial association between each class and the probability of wildfire occurrence. The weights were then used for training the ANFIS-GA and ANFIS-FA hybrid models. The models were validated using the ROC-AUC method that indicated that the ANFIS-GA model performed better (AUC(success rate) = 0.92; AUC(prediction rate) = 0.91) than the ANFIS-FA model (AUC(success rate) = 0.89; AUC(prediction rate) = 0.88). The efficiency of these models was compared to a single ANFIS model and statistical analyses of paired comparisons revealed that the two meta-optimized predictive models significantly improved wildfire prediction accuracy compared to the single ANFIS model (AUC(success rate) = 0.82; AUC(prediction rate) = 0.78). We concluded that such predictive models may become valuable toolldts to effectively guide fire management plans and on-the-ground decisions on firefighting strategies. |
WOS研究方向 | Environmental Sciences & Ecology |
来源期刊 | JOURNAL OF ENVIRONMENTAL MANAGEMENT
![]() |
文献类型 | 期刊论文 |
条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/101560 |
作者单位 | 1.AREEO, Res Inst Forests & Rangelands, Tehran, Iran; 2.KN Toosi Univ Technol, Fac Geodesy & Geomat Engn, Tehran, Iran; 3.Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam |
推荐引用方式 GB/T 7714 | Jaafari, Abolfazl,Termeh, Seyed Vahid Razavi,Dieu Tien Bui. Genetic and firefly metaheuristic algorithms for an optimized neuro-fuzzy prediction modeling of wildfire probability[J],2019,243:358-369. |
APA | Jaafari, Abolfazl,Termeh, Seyed Vahid Razavi,&Dieu Tien Bui.(2019).Genetic and firefly metaheuristic algorithms for an optimized neuro-fuzzy prediction modeling of wildfire probability.JOURNAL OF ENVIRONMENTAL MANAGEMENT,243,358-369. |
MLA | Jaafari, Abolfazl,et al."Genetic and firefly metaheuristic algorithms for an optimized neuro-fuzzy prediction modeling of wildfire probability".JOURNAL OF ENVIRONMENTAL MANAGEMENT 243(2019):358-369. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。