Genetic and firefly metaheuristic algorithms for an optimized neuro-fuzzy prediction modeling of wildfire probability.
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 describe...
| Publicado en: | Journal of Environmental Management Vol. 243; pp. 358 - 370 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Academic Press Inc.
Aug2019
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=136615536&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 136615536 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 03014797 EMJ jtl: Journal of Environmental Management issn: 03014797 maglogo: N pubinfo: dt: Aug2019 vid: 243 pid: 735 pub: Academic Press Inc. artinfo: ui: 136615536 10.1016/j.jenvman.2019.04.117 ppf: 358 ppct: 12 formats: tig: atl: Genetic and firefly metaheuristic algorithms for an optimized neuro-fuzzy prediction modeling of wildfire probability. aug: au: Jaafari, Abolfazl Razavi Termeh, Seyed Vahid Bui, Dieu Tien affil: Research Institute of Forests and Rangelands, Agricultural Research, Education, and Extension Organization (AREEO), Tehran, Iran Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam su: Human settlements Metaheuristic algorithms Wildfires Process optimization Prediction models Fire management sug: subj: Human settlements Metaheuristic algorithms Wildfires Process optimization Prediction models Fire management keyword: ANFIS Climate change GIS Metaheuristic algorithm Predictive modeling Zagros ecoregion ANFIS Climate change GIS Metaheuristic algorithm Predictive modeling Zagros ecoregion ab: 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 toolkits to effectively guide fire management plans and on-the-ground decisions on firefighting strategies. • Fine-tuning of ANFIS parameters using genetic and firefly optimization algorithms. • Overcoming the potential bias inherent in the over-fitted single ANFIS model. • Proving AUC>0.88 for wildfire prediction using the hybrid intelligence models. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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