Genetic and firefly metaheuristic algorithms for an optimized neuro-fuzzy prediction modeling of wildfire probability.
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Dieu Tien Bui | Abolfazl Jaafari | Seyed Vahid Razavi Termeh | D. Bui | A. Jaafari | S. V. Razavi Termeh
[1] Dieu Tien Bui,et al. Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility , 2019, CATENA.
[2] Pijush Samui,et al. A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping , 2019, CATENA.
[3] Bulent Tutmez,et al. Mapping forest fires by nonparametric clustering analysis , 2017, Journal of Forestry Research.
[4] Randall S. Sexton,et al. Comparing backpropagation with a genetic algorithm for neural network training , 1999 .
[5] Hamed Soleimani,et al. A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks , 2015 .
[6] J. Hanley,et al. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. , 1983, Radiology.
[7] E. Marchi,et al. Modeling anthropogenic and natural fire ignitions in an inner-alpine valley , 2017 .
[8] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .
[9] H. Pourghasemi,et al. Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. , 2018, The Science of the total environment.
[10] Mehdi Raftari,et al. Optimization of ANFIS with GA and PSO estimating α ratio in driven piles , 2019, Engineering with Computers.
[11] Kyle W. Rowden,et al. A novel triggerless approach for mass wasting susceptibility modeling applied to the Boston Mountains of Arkansas, USA , 2018, Natural Hazards.
[12] Jian Yang,et al. Spatial Patterns of Modern Period Human-Caused Fire Occurrence in the Missouri Ozark Highlands , 2007, Forest Science.
[13] Evan R. DeLancey,et al. The spatially varying influence of humans on fire probability in North America , 2016 .
[14] Binh Thai Pham,et al. Wildfire spatial pattern analysis in the Zagros Mountains, Iran: A comparative study of decision tree based classifiers , 2018, Ecol. Informatics.
[15] Mike D. Flannigan,et al. Anthropogenic influence on wildfire activity in Alberta, Canada , 2016 .
[16] Xin-She Yang,et al. Firefly Algorithms for Multimodal Optimization , 2009, SAGA.
[17] J. Hanley,et al. The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.
[18] Juan de la Riva,et al. An insight into machine-learning algorithms to model human-caused wildfire occurrence , 2014, Environ. Model. Softw..
[19] Dervis Karaboga,et al. Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey , 2018, Artificial Intelligence Review.
[20] F. Moreira,et al. Modeling and mapping wildfire ignition risk in Portugal , 2009 .
[21] Guy Pe'er,et al. Agricultural policy can reduce wildfires , 2018, Science.
[22] Dieu Tien Bui,et al. Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, Using GIS-Based Kernel Logistic Regression , 2016, Remote. Sens..
[23] F. Wilcoxon. Individual Comparisons by Ranking Methods , 1945 .
[24] Yongsheng Du,et al. Modeling Forest Lightning Fire Occurrence in the Daxinganling Mountains of Northeastern China with MAXENT , 2015 .
[25] Danial Jahed Armaghani,et al. Optimizing an ANN model with ICA for estimating bearing capacity of driven pile in cohesionless soil , 2018, Engineering with Computers.
[26] Anuradha Eaturu,et al. Biophysical and anthropogenic controls of forest fires in the Deccan Plateau, India. , 2008, Journal of environmental management.
[27] Biswajeet Pradhan,et al. Modification of landslide susceptibility mapping using optimized PSO-ANN technique , 2018, Engineering with Computers.
[28] Jyh-Shing Roger Jang,et al. ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..
[29] Dieu Tien Bui,et al. Wildfire Probability Mapping: Bivariate vs. Multivariate Statistics , 2019, Remote. Sens..
[30] Mohammad Bagher Menhaj,et al. A hybrid method for grade estimation using genetic algorithm and neural networks , 2009 .
[31] A-Xing Zhu,et al. Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. , 2018, The Science of the total environment.
[32] D. Bui,et al. Spatial Prediction of Rainfall-Induced Landslides Using Aggregating One-Dependence Estimators Classifier , 2018, Journal of the Indian Society of Remote Sensing.
[33] J. Greenberg,et al. Spatial variability in wildfire probability across the western United States , 2012 .
[34] Abolfazl Jaafari,et al. A Bayesian modeling of wildfire probability in the Zagros Mountains, Iran , 2017, Ecol. Informatics.
[35] Biswajeet Pradhan,et al. 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 , 2017 .
[36] K. Solaimani,et al. Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques , 2012, Natural Hazards.
[37] H. Pourghasemi,et al. GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran , 2014, International Journal of Environmental Science and Technology.
[38] A. Jaafari,et al. Spatial prediction of wildfire probability in the Hyrcanian ecoregion using evidential belief function model and GIS , 2018, International Journal of Environmental Science and Technology.
[39] Hamid Reza Pourghasemi,et al. Factors Influencing Regional-Scale Wildfire Probability in Iran , 2019, Spatial Modeling in GIS and R for Earth and Environmental Sciences.
[40] C. Willmott. Some Comments on the Evaluation of Model Performance , 1982 .
[41] Melanie Mitchell,et al. An introduction to genetic algorithms , 1996 .
[42] Atam P. Dhawan,et al. Use of genetic algorithms with backpropagation in training of feedforward neural networks , 1993, IEEE International Conference on Neural Networks.
[43] Hamid Reza Pourghasemi,et al. Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling , 2017 .
[44] Himan Shahabi,et al. Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability , 2019, Agricultural and Forest Meteorology.
[45] Zohre Sadat Pourtaghi,et al. Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran , 2015, Environmental Earth Sciences.
[46] H. Hong,et al. Predicting spatial patterns of wildfire susceptibility in the Huichang County, China: An integrated model to analysis of landscape indicators , 2019, Ecological Indicators.
[47] D. Riaño,et al. Multitemporal Modelling of Socio-Economic Wildfire Drivers in Central Spain between the 1980s and the 2000s: Comparing Generalized Linear Models to Machine Learning Algorithms , 2016, PloS one.
[48] Wei Chen,et al. Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility , 2019, CATENA.
[49] G. Bonham-Carter. Geographic Information Systems for Geoscientists: Modelling with GIS , 1995 .
[50] Sakuntala Mahapatra,et al. Induction Motor Control Using PSO-ANFIS☆ , 2015 .