Gene expression programming and ensemble methods for bushfire susceptibility mapping: a case study of Victoria, Australia

References

• BOM. 2021. Australia’s Official Weather Forecasts and Weather Radar - Bureau of Meteorology. [accessed 2020 Dec 01]. http://www.bom.gov.au/.
   Google Scholar
• Bui QT. 2019. Metaheuristic algorithms in optimizing neural network: a comparative study for forest fire susceptibility mapping in Dak Nong, Vietnam. Geomat Nat Hazards Risk. 10 (1):136–150.
   Web of Science ®Google Scholar
• Bunch A. 2020. Australia bushfire latest: state-by-state breakdown of all areas affected by blazes. [accessed 2021 Jan 01]. https://7news.com.au/news/bushfires/australia-bushfire-latest-state-by-state-breakdown-of-all-areas-affected-by-blazes-c-630858.
   Google Scholar
• Catry FX, Rego FC, Bação FL, Moreira F. 2009. Modeling and mapping wildfire ignition risk in Portugal. Int J Wildland Fire. 18(8):921–931.
   Web of Science ®Google Scholar
• Chen K, Jacobson C, Blong R. 2001. Using NDVI image texture analysis for bushfire-prone landscape assessment. Asian Conf Remote Sens. 22(9):1–9.
   Google Scholar
• Cheney NP. 1976. Bushfire disasters in Australia, 1945-1975. Aust For. 39(4):245–268.
   Google Scholar
• Clarke H, Gibson R, Cirulis B, Bradstock RA, Penman TD. 2019. Developing and testing models of the drivers of anthropogenic and lightning-caused wildfire ignitions in south-eastern Australia. J Environ Manage. 235:34–41.
   PubMed Web of Science ®Google Scholar
• Collins L, Bradstock RA, Penman TD. 2014. Can precipitation influence landscape controls on wildfire severity’ A case study within temperate eucalypt forests of south-eastern Australia. Int J Wildland Fire. 23(1):9–20.
   Web of Science ®Google Scholar
• Crimmins MA, Comrie AC. 2004. Interactions between antecedent climate and wildfire variability across south-eastern Arizona. Int J Wildland Fire. 13(4):455–466.
   Web of Science ®Google Scholar
• De Vasconcelos MJP, Silva S, Tome M, Alvim M, Pereira JMC. 2001. Spatial prediction of fire ignition probabilities: comparing logistic regression and neural networks. Photogramm Eng Remote Sensing. 67(1):73–81.
   Web of Science ®Google Scholar
• Dorji S, Ongsomwang S. 2017. Wildfire susceptibility mapping in Bhutan using geoinformatics technology. Suranaree J Sci Technol. 24(2):213–237.
   Web of Science ®Google Scholar
• Emamgolizadeh S, Bateni SM, Shahsavani D, Ashrafi T, Ghorbani H. 2015. Estimation of soil cation exchange capacity using Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS). J Hydrol. 529:1590–1600. https://doi.org/http://dx.doi.org/10.1016/j.jhydrol.2015.08.025.
   Web of Science ®Google Scholar
• Eskandari S, Amiri M, Sãdhasivam N, Pourghasemi HR. 2020a. Comparison of new individual and hybrid machine learning algorithms for modeling and mapping fire hazard: a supplementary analysis of fire hazard in different counties of Golestan Province in Iran. Nat Hazards. 104(1):305–327.
   Web of Science ®Google Scholar
• Eskandari S, Miesel JR, Pourghasemi HR. 2020b. The temporal and spatial relationships between climatic parameters and fire occurrence in northeastern Iran. Ecol Indic. 118:106720.
   Web of Science ®Google Scholar
• Ferreira C. 2001. Gene expression programming: a new adaptive algorithm for solving problems. [accessed 2021 Mar 8]. https://arxiv.org/abs/cs/0102027
   Google Scholar
• Ferreira C. 2013. Gepsoft GeneXproTools - Data modeling & analysis software. [accessed 2021 Mar 8]. https://www.gepsoft.com/.
   Google Scholar
• Gandomi AH, Tabatabaei SM, Moradian MH, Radfar A, Alavi AH. 2011. A new prediction model for the load capacity of castellated steel beams. J Constr Steel Res. 67(7):1096–1105. https://doi.org/http://dx.doi.org/10.1016/j.jcsr.2011.01.014.
   Web of Science ®Google Scholar
• Gholamnia K, Gudiyangada Nachappa T, Ghorbanzadeh O, Blaschke T. 2020. Comparisons of diverse machine learning approaches for wildfire susceptibility mapping. Symmetry. 12(4):604.
   Google Scholar
• Ghorbanzadeh O, Blaschke T, Gholamnia K, Aryal J. 2019a. Forest fire susceptibility and risk mapping using social/infrastructural vulnerability and environmental variables. Fire. 2(3):27–50.
   Google Scholar
• Ghorbanzadeh O, Feizizadeh B, Blaschke T. 2018. Multi-criteria risk evaluation by integrating an analytical network process approach into GIS-based sensitivity and uncertainty analyses. Geomat Nat Hazards Risk. 9(1):127–151.
   Web of Science ®Google Scholar
• Ghorbanzadeh O, Kamran KV, Blaschke T, Aryal J, Naboureh A, Einali J, Bian J. 2019b. Spatial prediction of wildfire susceptibility using field survey GPS data and machine learning approaches. Fire. 2(3):23–43.
   Google Scholar
• Gigović L, Pourghasemi HR, Drobnjak S, Bai S. 2019. Testing a new ensemble model based on SVM and random forest in forest fire susceptibility assessment and its mapping in Serbia’s Tara National Park. Forests. 10(5):408.
   Web of Science ®Google Scholar
• Gralewicz NJ, Nelson TA, Wulder MA. 2012. Factors influencing national scale wildfire susceptibility in Canada. For Ecol Manage. 265:20–29. https://doi.org/http://dx.doi.org/10.1016/j.foreco.2011.10.031.
   Web of Science ®Google Scholar
• Green ME, Deluca TF, Kaiser KWD. 2020. Modeling wildfire using evolutionary cellular automata. GECCO 2020 – Proceedings of the 2020 Genetic and Evolutionary Computation Conference; Cancún, Mexico. p. 1089–1097. https://doi.org/https://doi.org/10.1145/3377930.3389836
   Google Scholar
• Hoang ND, Tien Bui D. 2018. Spatial prediction of rainfall-induced shallow landslides using gene expression programming integrated with GIS: a case study in Vietnam. Nat Hazards. 92(3):1871–1887.
   Web of Science ®Google Scholar
• Hong H, Jaafari A, Zenner EK. 2019. Predicting spatial patterns of wildfire susceptibility in the Huichang County, China: an integrated model to analysis of landscape indicators. Ecol Indic. 101:878–891.
   Web of Science ®Google Scholar
• Hong H, Naghibi SA, Moradi Dashtpagerdi M, Pourghasemi HR, Chen W. 2017. A comparative assessment between linear and quadratic discriminant analyses (LDA-QDA) with frequency ratio and weights-of-evidence models for forest fire susceptibility mapping in China. Arab J Geosci. 10(7): 167.
   Web of Science ®Google Scholar
• Hong H, Tsangaratos P, Ilia I, Liu J, Zhu AX, Xu C. 2018. Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models. The case of Dayu County, China. Sci Total Environ. 630:1044–1056.
   PubMed Web of Science ®Google Scholar
• Jaafari A, Gholami DM, Zenner EK. 2017. A Bayesian modeling of wildfire probability in the Zagros Mountains, Iran. Ecol Inform. 39:32–44.
   Web of Science ®Google Scholar
• Jaafari A, Mafi-Gholami D, Thai Pham B, Tien Bui D. 2019c. Wildfire probability mapping: Bivariate vs. Multivariate statistics. Remote Sens. 11(6):618.
   Web of Science ®Google Scholar
• Jaafari A, Pourghasemi HR. 2019. Factors influencing regional-scale wildfire probability in Iran. In: In Spatial modeling in GIS and R for Earth and environmental sciences. Elsevier; p. 607–619. https://doi.org/https://doi.org/10.1016/b978-0-12-815226-3.00028-4.
   Google Scholar
• Jaafari A, Razavi Termeh SV, Bui DT. 2019a. Genetic and firefly metaheuristic algorithms for an optimized neuro-fuzzy prediction modeling of wildfire probability. J Environ Manage. 243:358–369.
   PubMed Web of Science ®Google Scholar
• Jaafari A, Zenner EK, Panahi M, Shahabi H. 2019b. Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability. Agric for Meteorol. 266–267:198–207.
   Web of Science ®Google Scholar
• Jaafari A, Zenner EK, Pham BT. 2018. Wildfire spatial pattern analysis in the Zagros Mountains, Iran: a comparative study of decision tree based classifiers. Ecol Inform. 43:200–211.
   Web of Science ®Google Scholar
• Jain P, Coogan SCP, Subramanian SG, Crowley M, Taylor S, Flannigan MD. 2020. A review of machine learning applications in wildfire science and management. Environ. Rev.28(4): 478-505.https://doi.org/https://doi.org/10.1139/er-2020-0019
   Google Scholar
• Kayadelen C. 2011. Soil liquefaction modeling by genetic expression programming and neuro-fuzzy. Expert Syst Appl. 38(4):4080–4087.
   Web of Science ®Google Scholar
• Koza JR. 1992. Genetic programming : On the programming of computers by means of natural selection complex adaptive systems. (Vol. 1). MIT press.
   Google Scholar
• Lee S, Pradhan B. 2007. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides. 4(1):33–41.
   Web of Science ®Google Scholar
• Leonard J, Blanchi R. 2012. Queensland Bushfire Risk Planning Project. Stage 1 Report for the Department of Community Safety–Strategic Policy Division Final Version–Approved by CSIRO, July.
   Google Scholar
• Leuenberger M, Parente J, Tonini M, Pereira MG, Kanevski M. 2018. Wildfire susceptibility mapping: deterministic vs. stochastic approaches. Environ Model Softw. 101:194–203.
   Web of Science ®Google Scholar
• Ling CX, Huang J, Zhang H. 2003. AUC: a statistically consistent and more discriminating measure than accuracy. IJCAI Int Jt Conf Artif Intell. 3: 519–524.
   Google Scholar
• Malak DA, Pausas JG. 2006. Fire regime and post-fire Normalized Difference Vegetation Index changes in the eastern Iberian Peninsula (Mediterranean basin). Int J Wildland Fire. 15(3):407–413.
   Web of Science ®Google Scholar
• Moayedi H, Mehrabi M, Bui DT, Pradhan B, Foong LK. 2020. Fuzzy-metaheuristic ensembles for spatial assessment of forest fire susceptibility. J Environ Manage. 260:109867.
   PubMed Web of Science ®Google Scholar
• Mousavi SM, Aminian P, Gandomi AH, Alavi AH, Bolandi H. 2012. A new predictive model for compressive strength of HPC using gene expression programming. Adv Eng Softw. 45(1):105–114. https://doi.org/http://dx.doi.org/10.1016/j.advengsoft.2011.09.014.
   Web of Science ®Google Scholar
• Nicholls N, Lucas C. 2007. Interannual variations of area burnt in Tasmanian bushfires: relationships with climate and predictability. Int J Wildland Fire. 16(5):540–546.
   Web of Science ®Google Scholar
• O’Connor CD, Thompson MP, Rodríguez y Silva F. 2016. Getting ahead of the wildfire problem: quantifying and mapping management challenges and opportunities. Geosciences. 6(3):35.
   Web of Science ®Google Scholar
• OSM. 2021. OpenStreetMap. [accessed 2020 Dec 01]. https://www.openstreetmap.org/#map=4/-28.15/133.28.
   Google Scholar
• Pham BT, Jaafari A, Avand M, Al-Ansari N, Du T,H, Yen HP, Phong T, Van Nguyen DH, Van Le H, Mafi-Gholami D, et al. 2020. Performance evaluation of machine learning methods for forest fire modeling and prediction. Symmetry (Basel). 12(6):1022.
   Google Scholar
• Pourghasemi HR. 2016. GIS-based forest fire susceptibility mapping in Iran: a comparison between evidential belief function and binary logistic regression models. Scand J for Res. 31(1):80–98.
   Web of Science ®Google Scholar
• R Core Team. 2020. R: a language and environment for statistical computing. Vienna (Austria): R Foundation for Statistical Computing. [accessed 2021 Dec 05]. https://www.R-project.org/.
   Google Scholar
• Razavi-Termeh SV, Sadeghi-Niaraki A, Choi SM. 2020. Ubiquitous GIS-based forest fire susceptibility mapping using artificial intelligence methods. Remote Sens. 12(10):1689.
   Google Scholar
• Sachdeva S, Bhatia T, Verma AK. 2018. GIS-based evolutionary optimized Gradient Boosted Decision Trees for forest fire susceptibility mapping. Nat Hazards. 92(3):1399–1418.
   Web of Science ®Google Scholar
• Shang C, Wulder MA, Coops NC, White JC, Hermosilla T. 2020. Spatially-explicit prediction of wildfire burn probability using remotely-sensed and ancillary data. Can J Remote Sens. 46(3):1–17.
   Web of Science ®Google Scholar
• Tehrany MS, Jones S, Shabani F, Martínez-Álvarez F, Tien Bui D. 2019. A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data. Theor Appl Climatol. 137(1–2):637–653.
   Web of Science ®Google Scholar
• Tien Bui D, Bui QT, Nguyen QP, Pradhan B, Nampak H, Trinh PT. 2017. 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. Agric For Meteorol. 233:32–44. https://doi.org/http://dx.doi.org/10.1016/j.agrformet.2016.11.002.
   Web of Science ®Google Scholar
• Tien Bui D, Le KTT, Nguyen VC, Le HD, Revhaug I. 2016. Tropical forest fire susceptibility mapping at the Cat Ba National Park area, Hai Phong City, Vietnam, using GIS-based Kernel logistic regression. Remote Sens. 8(4):315–347.
   Google Scholar
• Tolhurst KG, Shields B, Chong DM. 2008. Phoenix: development and application of a bushfire risk management tool. Aust J Emerg Manag. 23(4):47.
   Google Scholar
• Tonini M, D’andrea M, Biondi G, Esposti SD, Trucchia A, Fiorucci P. 2020. A machine learning-based approach for wildfire susceptibility mapping. The case study of the Liguria region in Italy. Geosciences. 10(3): 105.
   PubMed Web of Science ®Google Scholar
• USGS. 2021. EarthExplorer. [accessed 2020 Dec 01]. https://earthexplorer.usgs.gov.
   Google Scholar
• Valdez MC, Chang KT, Chen CF, Chiang SH, Santos JL. 2017. Modelling the spatial variability of wildfire susceptibility in Honduras using remote sensing and geographical information systems. Geomat Nat Hazards Risk. 8(2):876–892.
   Web of Science ®Google Scholar
• Van Westen CJ, Rengers N, Soeters R. 2003. Use of geomorphological expert knowledge in indirect landslide hazard assessment. Nat Hazards. 30(3):399–419.
   Web of Science ®Google Scholar
• Werner J, Lyons S. 2020. The size of Australia’s bushfire crisis captured in five big numbers. ABC Sci. [accessed 2021 Mar 8]. https://www.abc.net.au/news/science/2020-03-05/bushfire-crisis-five-big-numbers/12007716.
   Google Scholar
• You W, Lin L, Wu L, Ji Z, Yu J, Zhu J, Fan Y, He D. 2017. Geographical information system-based forest fire risk assessment integrating national forest inventory data and analysis of its spatiotemporal variability. Ecol Indic. 77:176–184. https://doi.org/http://dx.doi.org/10.1016/j.ecolind.2017.01.042.
   Web of Science ®Google Scholar
• Zakaria NA, Azamathulla HM, Chang CK, Ghani AA. 2010. Gene expression programming for total bed material load estimation-a case study. Sci Total Environ. 408(21):5078–5085. https://doi.org/http://dx.doi.org/10.1016/j.scitotenv.2010.07.048.
   PubMed Web of Science ®Google Scholar
• Zhang G, Wang M, Liu K. 2019. Forest fire susceptibility modeling using a convolutional neural network for Yunnan Province of China. Int J Disaster Risk Sci. 10(3):386–403.
   Web of Science ®Google Scholar
• Zhang Y, Lim S, Sharples JJ. 2015. Development of spatial models for bushfire occurrence in South-Eastern Australia. 21st International Congress on Modelling and Simulation, Gold Coast, Australia. https://116.90.59.164/modsim2015/A4/zhang.pdf.
   Google Scholar
• Zhang Y, Lim S, Sharples JJ. 2016. Modelling spatial patterns of wildfire occurrence in South-Eastern Australia. Geomat Nat Hazards Risk. 7(6):1800–1815.
   Web of Science ®Google Scholar