{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T04:47:47Z","timestamp":1779252467357,"version":"3.51.4"},"reference-count":99,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,6,17]],"date-time":"2020-06-17T00:00:00Z","timestamp":1592352000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Vietnam National Foundation for Science and Technology Development","award":["105.08-2019.03"],"award-info":[{"award-number":["105.08-2019.03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Predicting and mapping fire susceptibility is a top research priority in fire-prone forests worldwide. This study evaluates the abilities of the Bayes Network (BN), Na\u00efve Bayes (NB), Decision Tree (DT), and Multivariate Logistic Regression (MLP) machine learning methods for the prediction and mapping fire susceptibility across the Pu Mat National Park, Nghe An Province, Vietnam. The modeling methodology was formulated based on processing the information from the 57 historical fires and a set of nine spatially explicit explanatory variables, namely elevation, slope degree, aspect, average annual temperate, drought index, river density, land cover, and distance from roads and residential areas. Using the area under the receiver operating characteristic curve (AUC) and seven other performance metrics, the models were validated in terms of their abilities to elucidate the general fire behaviors in the Pu Mat National Park and to predict future fires. Despite a few differences between the AUC values, the BN model with an AUC value of 0.96 was dominant over the other models in predicting future fires. The second best was the DT model (AUC = 0.94), followed by the NB (AUC = 0.939), and MLR (AUC = 0.937) models. Our robust analysis demonstrated that these models are sufficiently robust in response to the training and validation datasets change. Further, the results revealed that moderate to high levels of fire susceptibilities are associated with ~19% of the Pu Mat National Park where human activities are numerous. This study and the resultant susceptibility maps provide a basis for developing more efficient fire-fighting strategies and reorganizing policies in favor of sustainable management of forest resources.<\/jats:p>","DOI":"10.3390\/sym12061022","type":"journal-article","created":{"date-parts":[[2020,6,18]],"date-time":"2020-06-18T12:21:46Z","timestamp":1592482906000},"page":"1022","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":229,"title":["Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9707-840X","authenticated-orcid":false,"given":"Binh Thai","family":"Pham","sequence":"first","affiliation":[{"name":"University of Transport Technology, Hanoi 100000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3441-6560","authenticated-orcid":false,"given":"Abolfazl","family":"Jaafari","sequence":"additional","affiliation":[{"name":"Research Institute of Forests and Rangelands, Agricultural Research, Education, and Extension Organization (AREEO), Tehran 64414-356, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7196-5051","authenticated-orcid":false,"given":"Mohammadtaghi","family":"Avand","sequence":"additional","affiliation":[{"name":"Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Tehran 14115-111, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6790-2653","authenticated-orcid":false,"given":"Nadhir","family":"Al-Ansari","sequence":"additional","affiliation":[{"name":"Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187 Lulea, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tran","family":"Dinh Du","sequence":"additional","affiliation":[{"name":"Department of Land Management, School of Agriculture and Resources, Vinh University, Nghe An 43000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hoang Phan Hai","family":"Yen","sequence":"additional","affiliation":[{"name":"Department of Geography, School of Social Education Vinh University, Nghe An 43000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2982-8309","authenticated-orcid":false,"given":"Tran Van","family":"Phong","sequence":"additional","affiliation":[{"name":"Institute of Geological Sciences, Vietnam Academy of Sciences and Technology, 84 Chua Lang Street, Dong da, Hanoi 100000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Duy Huu","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Faculty of Geography, VNU University of Science, Vietnam National University, 334 Nguyen Trai, Ha Noi 100000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiep Van","family":"Le","sequence":"additional","affiliation":[{"name":"Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4431-5381","authenticated-orcid":false,"given":"Davood","family":"Mafi-Gholami","sequence":"additional","affiliation":[{"name":"Department of Forest Sciences, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord 8818634141, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Indra","family":"Prakash","sequence":"additional","affiliation":[{"name":"Department of Science &amp; Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar 382002, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hoang","family":"Thi Thuy","sequence":"additional","affiliation":[{"name":"Department of Resource and Environment Management, School of Agriculture and Resources, Vinh University, Nghe An 43000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tran Thi","family":"Tuyen","sequence":"additional","affiliation":[{"name":"Department of Resource and Environment Management, School of Agriculture and Resources, Vinh University, Nghe An 43000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1126\/science.1163886","article-title":"Fire in the Earth system","volume":"324","author":"Bowman","year":"2009","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1038\/s41558-020-0716-1","article-title":"Unprecedented burn area of Australian mega forest fires","volume":"10","author":"Boer","year":"2020","journal-title":"Nat. Clim. Chang."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Meng, Y., Deng, Y., and Shi, P. (2015). Mapping forest wildfire risk of the world. World Atlas of Natural Disaster Risk, Springer.","DOI":"10.1007\/978-3-662-45430-5_14"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1890\/120332","article-title":"Temperate and boreal forest mega-fires: Characteristics and challenges","volume":"12","author":"Stephens","year":"2014","journal-title":"Front. Ecol. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1016\/j.egyr.2019.11.002","article-title":"Urban air pollution, climate change and wildfires: The case study of an extended forest fire episode in northern Italy favoured by drought and warm weather conditions","volume":"6","author":"Bo","year":"2020","journal-title":"Energy Rep."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1007\/s10661-018-7052-1","article-title":"Assessing fire hazard potential and its main drivers in Mazandaran province, Iran: A data-driven approach","volume":"190","author":"Adab","year":"2018","journal-title":"Environ. Monit. Assess."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.envsoft.2014.03.003","article-title":"An insight into machine-learning algorithms to model human-caused wildfire occurrence","volume":"57","author":"Rodrigues","year":"2014","journal-title":"Environ. Model. Softw."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1080\/19475705.2015.1084541","article-title":"Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem","volume":"7","author":"Satir","year":"2016","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1071\/WF07087","article-title":"Predicting spatial patterns of fire on a southern California landscape","volume":"17","author":"Syphard","year":"2008","journal-title":"Int. J. Wildland Fire"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.ecoinf.2018.08.008","article-title":"GIS-based spatial prediction of tropical forest fire danger using a new hybrid machine learning method","volume":"48","author":"Le","year":"2018","journal-title":"Ecol. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"17797","DOI":"10.1038\/s41598-018-36134-4","article-title":"Wildfires and the role of their drivers are changing over time in a large rural area of west-central Spain","volume":"8","author":"Viedma","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1007\/s13753-019-00233-1","article-title":"Forest Fire Susceptibility Modeling Using a Convolutional Neural Network for Yunnan Province of China","volume":"10","author":"Zhang","year":"2019","journal-title":"Int. J. Disaster Risk Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s13280-018-1085-0","article-title":"Identifying hotspots of land use cover change under socioeconomic and climate change scenarios in Mexico","volume":"48","author":"Galicia","year":"2019","journal-title":"Ambio"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Martinho, V.J.P.D. (2019). Socioeconomic Impacts of Forest Fires upon Portugal: An Analysis for the Agricultural and Forestry Sectors. Sustainability, 11.","DOI":"10.3390\/su11020374"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"105972","DOI":"10.1016\/j.ecolind.2019.105972","article-title":"Mangrove regional feedback to sea level rise and drought intensity at the end of the 21st century","volume":"110","author":"Zenner","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1326","DOI":"10.1016\/j.scitotenv.2018.11.462","article-title":"Modeling multi-decadal mangrove leaf area index in response to drought along the semi-arid southern coasts of Iran","volume":"656","author":"Zenner","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"109628","DOI":"10.1016\/j.jenvman.2019.109628","article-title":"Multi-hazards vulnerability assessment of southern coasts of Iran","volume":"252","author":"Zenner","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106644","DOI":"10.1016\/j.ecss.2020.106644","article-title":"Spatially explicit predictions of changes in the extent of mangroves of Iran at the end of the 21st century","volume":"237","author":"Zenner","year":"2020","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.scitotenv.2019.05.298","article-title":"Drought in Portugal: Current regime, comparison of indices and impacts on extreme wildfires","volume":"685","author":"Parente","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.ecoinf.2017.03.003","article-title":"A Bayesian modeling of wildfire probability in the Zagros Mountains, Iran","volume":"39","author":"Jaafari","year":"2017","journal-title":"Ecol. Inform."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1007\/s11676-018-0666-x","article-title":"Determination of fire risk to assist fire management for insular areas: The case of a small Greek island","volume":"30","author":"Sakellariou","year":"2019","journal-title":"J. For. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2928","DOI":"10.1002\/2014GL059576","article-title":"Large wildfire trends in the western United States, 1984\u20132011","volume":"41","author":"Dennison","year":"2014","journal-title":"Geophys. Res. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1007\/s10661-017-6225-7","article-title":"Forest fire risk assessment-an integrated approach based on multicriteria evaluation","volume":"189","author":"Goleiji","year":"2017","journal-title":"Environ. Monit. Assess."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Jaafari, A., Mafi-Gholami, D., Pham, B.T., and Tien Bui, D. (2019). Wildfire probability mapping: Bivariate vs. multivariate statistics. Remote Sens., 11.","DOI":"10.3390\/rs11060618"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.envpol.2014.07.023","article-title":"Vegetation fires and air pollution in Vietnam","volume":"195","author":"Le","year":"2014","journal-title":"Environ. Pollut."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.foreco.2012.03.003","article-title":"Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest","volume":"275","author":"Oliveira","year":"2012","journal-title":"For. Ecol. Manag."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.agrformet.2016.11.002","article-title":"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","volume":"233","author":"Bui","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1016\/j.jenvman.2019.01.108","article-title":"Spatial pattern analysis and prediction of forest fire using new machine learning approach of Multivariate Adaptive Regression Splines and Differential Flower Pollination optimization: A case study at Lao Cai province (Viet Nam)","volume":"237","author":"Hoang","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"117723","DOI":"10.1016\/j.foreco.2019.117723","article-title":"A Bayesian network model for prediction and analysis of possible forest fire causes","volume":"457","author":"Sevinc","year":"2020","journal-title":"For. Ecol. Manag."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.crm.2017.01.004","article-title":"A novel method to identify likely causes of wildfire","volume":"16","author":"Mhawej","year":"2017","journal-title":"Clim. Risk Manag."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.ecoinf.2017.12.006","article-title":"Wildfire spatial pattern analysis in the Zagros Mountains, Iran: A comparative study of decision tree based classifiers","volume":"43","author":"Jaafari","year":"2018","journal-title":"Ecol. Inform."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gholamnia, K., Gudiyangada Nachappa, T., Ghorbanzadeh, O., and Blaschke, T. (2020). Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping. Symmetry, 12.","DOI":"10.3390\/sym12040604"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.ecoinf.2018.05.009","article-title":"Spatial pattern assessment of tropical forest fire danger at Thuan Chau area (Vietnam) using GIS-based advanced machine learning algorithms: A comparative study","volume":"46","author":"Thach","year":"2018","journal-title":"Ecol. Inform."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1007\/s12524-016-0557-6","article-title":"Fire risk assessment using neural network and logistic regression","volume":"44","author":"Goldarag","year":"2016","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.jenvman.2019.04.117","article-title":"Genetic and firefly metaheuristic algorithms for an optimized neuro-fuzzy prediction modeling of wildfire probability","volume":"243","author":"Jaafari","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.agrformet.2018.12.015","article-title":"Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability","volume":"266\u2013267","author":"Jaafari","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"109867","DOI":"10.1016\/j.jenvman.2019.109867","article-title":"Fuzzy-metaheuristic ensembles for spatial assessment of forest fire susceptibility","volume":"260","author":"Moayedi","year":"2020","journal-title":"J. Environ. Manag."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Jaafari, A., and Pourghasemi, H.R. (2019). Factors Influencing Regional-Scale Wildfire Probability in Iran: An Application of Random Forest and Support Vector Machine. Spatial Modeling in GIS and R for Earth and Environmental Sciences, Elsevier.","DOI":"10.1016\/B978-0-12-815226-3.00028-4"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1071\/WF09030","article-title":"A model for predicting human-caused wildfire occurrence in the region of Madrid, Spain","volume":"19","author":"Vilar","year":"2010","journal-title":"Int. J. Wildland Fire"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1007\/s00704-018-2628-9","article-title":"A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data","volume":"137","author":"Tehrany","year":"2019","journal-title":"Theor. Appl. Climatol."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Pham, B.T., Prakash, I., Khosravi, K., Chapi, K., Trinh, P.T., Ngo, T.Q., Hosseini, S.V., and Bui, D.T. (2018). A comparison of Support Vector Machines and Bayesian algorithms for landslide susceptibility modelling. Geocarto Int.","DOI":"10.1080\/10106049.2018.1489422"},{"key":"ref_42","first-page":"71","article-title":"Evaluation of predictive ability of support vector machines and naive Bayes trees methods for spatial prediction of landslides in Uttarakhand state (India) using GIS","volume":"10","author":"Pham","year":"2016","journal-title":"J. Geomat."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1002\/ldr.3255","article-title":"Sinkhole susceptibility mapping: A comparison between Bayes-based machine learning algorithms","volume":"30","author":"Taheri","year":"2019","journal-title":"Land Degrad. Dev."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1006","DOI":"10.1016\/j.scitotenv.2018.06.389","article-title":"Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and na\u00efve Bayes tree for landslide susceptibility modeling","volume":"644","author":"Chen","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2163","DOI":"10.1080\/19475705.2019.1685010","article-title":"Assessing urban flood disaster risk using Bayesian network model and GIS applications","volume":"10","author":"Wu","year":"2019","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Vetrita, Y., and Cochrane, M.A. (2020). Fire Frequency and Related Land-Use and Land-Cover Changes in Indonesia\u2019s Peatlands. Remote Sens., 12.","DOI":"10.3390\/rs12010005"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1016\/j.ecolind.2019.01.056","article-title":"Predicting spatial patterns of wildfire susceptibility in the Huichang County, China: An integrated model to analysis of landscape indicators","volume":"101","author":"Hong","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s13762-017-1371-6","article-title":"Spatial prediction of wildfire probability in the Hyrcanian ecoregion using evidential belief function model and GIS","volume":"15","author":"Nami","year":"2018","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.rse.2004.01.019","article-title":"Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating","volume":"92","author":"Chuvieco","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1023\/A:1011641601076","article-title":"Flammability assessment of Mediterranean forest fuels","volume":"37","author":"Dimitrakopoulos","year":"2001","journal-title":"Fire Technol."},{"key":"ref_51","first-page":"1","article-title":"21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions","volume":"9","author":"Anderson","year":"2018","journal-title":"Nat. Commun."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1175\/2009JCLI2900.1","article-title":"Use of NDVI and land surface temperature for drought assessment: Merits and limitations","volume":"23","author":"Karnieli","year":"2010","journal-title":"J. Clim."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.jenvman.2018.07.098","article-title":"A comprehensive spatial-temporal analysis of driving factors of human-caused wildfires in Spain using geographically weighted logistic regression","volume":"225","author":"Rodrigues","year":"2018","journal-title":"J. Environ. Manag."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"075005","DOI":"10.1088\/1748-9326\/11\/7\/075005","article-title":"The spatially varying influence of humans on fire probability in North America","volume":"11","author":"Parisien","year":"2016","journal-title":"Environ. Res. Lett."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1007\/s00267-014-0269-z","article-title":"Modeling the landscape drivers of fire recurrence in Sardinia (Italy)","volume":"53","author":"Ricotta","year":"2014","journal-title":"Environ. Manag."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1071\/WF19070","article-title":"Likelihood and frequency of recurrent fire ignitions in highly urbanised Mediterranean landscapes","volume":"29","author":"Elia","year":"2020","journal-title":"Int. J. Wildland Fire"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Kira, K., and Rendell, L.A. (1992). A practical approach to feature selection. Machine Learning Proceedings 1992, Elsevier.","DOI":"10.1016\/B978-1-55860-247-2.50037-1"},{"key":"ref_58","unstructured":"Kononenko, I. (1995, January 25\u201327). Estimating attributes: Analysis and extensions of RELIEF. Proceedings of the European Conference on Machine Learning, Catania, Italy."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"104451","DOI":"10.1016\/j.catena.2019.104451","article-title":"A spatially explicit deep learning neural network model for the prediction of landslide susceptibility","volume":"188","author":"Dao","year":"2020","journal-title":"CATENA"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1023\/A:1007465528199","article-title":"Bayesian network classifiers","volume":"29","author":"Friedman","year":"1997","journal-title":"Mach. Learn."},{"key":"ref_61","unstructured":"Cheng, J., and Greiner, R. (August, January 30). Comparing Bayesian network classifiers. Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, San Francisco, CA, USA."},{"key":"ref_62","unstructured":"Davies, P. (2007). Bayesian Decision Networks for Management of High Conservation Assets (National Water Initiative\u2013Australian Government Water Fund, Report 6\/6 Report to the Conservation of Freshwater Ecosystem Values Project."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.envsoft.2016.07.005","article-title":"A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India)","volume":"84","author":"Pham","year":"2016","journal-title":"Environ. Model. Softw."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1457","DOI":"10.1007\/s12524-018-0791-1","article-title":"Spatial Prediction of Rainfall-Induced Landslides Using Aggregating One-Dependence Estimators Classifier","volume":"46","author":"Pham","year":"2018","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Debeljak, M., and D\u017eeroski, S. (2011). Decision trees in ecological modelling. Modelling Complex Ecological Dynamics, Springer.","DOI":"10.1007\/978-3-642-05029-9_14"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Wang, Y., Witten, I., van Someren, M., and Widmer, G. (,  1997). Inducing models trees for continuous classes. Proceedings of the Poster Papers of the European Conference on Machine Learning, Department of Computer Science, University of Waikato, Hamilton, New Zealand.","DOI":"10.1007\/3-540-62858-4"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Nguyen, P.T., Ha, D.H., Jaafari, A., Nguyen, H.D., Van Phong, T., Al-Ansari, N., Prakash, I., Le, H.V., and Pham, B.T. (2020). Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17072473"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Nguyen, P.T., Ha, D.H., Avand, M., Jaafari, A., Nguyen, H.D., Al-Ansari, N., Phong, T.V., Sharma, R., Kumar, R., and Le, H.V. (2020). Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping. Appl. Sci., 10.","DOI":"10.3390\/app10072469"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1007\/s12665-017-7207-3","article-title":"LiDAR-supported prediction of slope failures using an integrated ensemble weights-of-evidence and analytical hierarchy process","volume":"77","author":"Jaafari","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Janizadeh, S., Avand, M., Jaafari, A., Phong, T.V., Bayat, M., Ahmadisharaf, E., Prakash, I., Pham, B.T., and Lee, S. (2019). Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran. Sustainability, 11.","DOI":"10.3390\/su11195426"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Chen, W., Hong, H., Panahi, M., Shahabi, H., Wang, Y., Shirzadi, A., Pirasteh, S., Alesheikh, A.A., Khosravi, K., and Panahi, S. (2019). Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO). Appl. Sci., 9.","DOI":"10.3390\/app9183755"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Avand, M., Janizadeh, S., Tien Bui, D., Pham, V.H., Ngo, P.T.T., and Nhu, V.-H. (2020). A tree-based intelligence ensemble approach for spatial prediction of potential groundwater. Int. J. Digit. Earth, 1\u201322.","DOI":"10.1080\/17538947.2020.1718785"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Hosmer, D.W., Lemeshow, S., and Sturdivant, R.X. (2013). Applied Logistic Regression, John Wiley & Sons.","DOI":"10.1002\/9781118548387"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Caruana, R., and Niculescu-Mizil, A. (2004, January 22\u201325). Data mining in metric space: An empirical analysis of supervised learning performance criteria. Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, WC, USA.","DOI":"10.1145\/1014052.1014063"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Lavesson, N., and Davidsson, P. (2008, January 24\u201326). Generic methods for multi-criteria evaluation. Proceedings of the 2008 SIAM International Conference on Data Mining, Atlanta, GA, USA.","DOI":"10.1137\/1.9781611972788.49"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Nhu, V.-H., Shirzadi, A., Shahabi, H., Chen, W., Clague, J.J., Geertsema, M., Jaafari, A., Avand, M., Miraki, S., and Asl, D.T. (2020). Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and its Ensembles in a Semi-Arid Region of Iran. Forests, 11.","DOI":"10.3390\/f11040421"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Nhu, V.-H., Janizadeh, S., Avand, M., Chen, W., Farzin, M., Omidvar, E., Shirzadi, A., Shahabi, H., Clague, J.J., and Jaafari, A. (2020). GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models. Appl. Sci., 10.","DOI":"10.3390\/app10062039"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Bui, D.T., Moayedi, H., G\u00f6r, M., Jaafari, A., and Foong, L.K. (2019). Predicting slope stability failure through machine learning paradigms. Isprs Int. J. Geo Inf., 8.","DOI":"10.3390\/ijgi8090395"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"117320","DOI":"10.1016\/j.atmosenv.2020.117320","article-title":"Hybridized neural fuzzy ensembles for dust source modeling and prediction","volume":"224","author":"Rahmati","year":"2020","journal-title":"Atmos. Environ."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1080\/10106049.2016.1188166","article-title":"Applicability of generalized additive model in groundwater potential modelling and comparison its performance by bivariate statistical methods","volume":"32","author":"Falah","year":"2017","journal-title":"Geocarto Int."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.1016\/j.scitotenv.2016.06.176","article-title":"Application of Dempster\u2013Shafer theory, spatial analysis and remote sensing for groundwater potentiality and nitrate pollution analysis in the semi-arid region of Khuzestan, Iran","volume":"568","author":"Rahmati","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1007\/s11269-015-1215-4","article-title":"Assessing the Accuracy of GIS-Based Analytical Hierarchy Process for Watershed Prioritization; Gorganrood River Basin, Iran","volume":"30","author":"Rahmati","year":"2016","journal-title":"Water Resour. Manag."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Pham, B.T., Jaafari, A., Prakash, I., Singh, S.K., Quoc, N.K., and Bui, D.T. (2019). Hybrid computational intelligence models for groundwater potential mapping. Catena, 182.","DOI":"10.1016\/j.catena.2019.104101"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"137612","DOI":"10.1016\/j.scitotenv.2020.137612","article-title":"Improving prediction of water quality indices using novel hybrid machine-learning algorithms","volume":"721","author":"Bui","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Rahmati, O., Panahi, M., Kalantari, Z., Soltani, E., Falah, F., Dayal, K.S., Mohammadi, F., Deo, R.C., Tiefenbacher, J., and Tien Bui, D. (2019). Capability and robustness of novel hybridized models used for drought hazard modeling in southeast Queensland, Australia. Sci. Total Environ.","DOI":"10.1016\/j.scitotenv.2019.134656"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Tien Bui, D., Le, K.-T.T., Nguyen, V.C., Le, H.D., and 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.","DOI":"10.3390\/rs8040347"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1071\/WF11178","article-title":"Wildfire ignition-distribution modelling: A comparative study in the Huron\u2013Manistee National Forest, Michigan, USA","volume":"22","author":"Massada","year":"2013","journal-title":"Int. J. Wildland Fire"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.foreco.2011.10.031","article-title":"Factors influencing national scale wildfire susceptibility in Canada","volume":"265","author":"Gralewicz","year":"2012","journal-title":"For. Ecol. Manag."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Kocher, S.D., and Butsic, V. (2017). Governance of land use planning to reduce fire risk to homes Mediterranean France and California. Land, 6.","DOI":"10.3390\/land6020024"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1006\/jema.2002.0545","article-title":"Factors influencing fire behaviour in shrublands of different stand ages and the implications for using prescribed burning to reduce wildfire risk","volume":"65","author":"Baeza","year":"2002","journal-title":"J. Environ. Manag."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1007\/s10980-005-0070-8","article-title":"Land cover type and fire in Portugal: Do fires burn land cover selectively?","volume":"20","author":"Nunes","year":"2005","journal-title":"Landsc. Ecol."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Bayat, M., Ghorbanpour, M., Zare, R., Jaafari, A., and Thai Pham, B. (2019). Application of artificial neural networks for predicting tree survival and mortality in the Hyrcanian forest of Iran. Comput. Electron. Agric., 164.","DOI":"10.1016\/j.compag.2019.104929"},{"key":"ref_93","unstructured":"McCune, B., Grace, J.B., and Urban, D.L. (2002). Analysis of Ecological Communities, MjM Software Design."},{"key":"ref_94","first-page":"73","article-title":"Spatial prediction of fire ignition probabilities: Comparing logistic regression and neural networks","volume":"67","author":"Silva","year":"2001","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.catena.2018.12.033","article-title":"Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility","volume":"175","author":"Jaafari","year":"2019","journal-title":"Catena"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1049\/trit.2019.0053","article-title":"Guest Editorial: Advances in Bio-inspired Heuristics for Computing","volume":"4","author":"Zhao","year":"2019","journal-title":"Caai Trans. Intell. Technol."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1049\/trit.2019.0063","article-title":"Guest Editorial: Rough Sets and Data Mining","volume":"4","author":"Sakai","year":"2019","journal-title":"Caai Trans. Intell. Technol."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Wei, X., and Larsen, C.P.S. (2019). Methods to Detect Edge Effected Reductions in Fire Frequency in Simulated Forest Landscapes. ISPRS Int. J. Geo Inf., 8.","DOI":"10.3390\/ijgi8060277"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"5937","DOI":"10.1002\/ece3.4076","article-title":"Edge effects in fire-prone landscapes: Ecological importance and implications for fauna","volume":"8","author":"Parkins","year":"2018","journal-title":"Ecol. Evol."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/6\/1022\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:39:44Z","timestamp":1760175584000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/6\/1022"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,17]]},"references-count":99,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["sym12061022"],"URL":"https:\/\/doi.org\/10.3390\/sym12061022","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,17]]}}}