[
Akıncı HA, Akıncı H, (2023), Machine learning based forest fire susceptibility assessment of Manavgat district (Antalya. Turkey. Earth Science Informatics, 16, (1):397–414. doi:10.1007/s12145-023-00953-5.
]Search in Google Scholar
[
Aldiansyah S, Wardani F, (2023), Evaluation of flood susceptibility prediction based on a resampling method using machine learning. Journal of Water and Climate Change, 14, (3):937–961. doi: 10.2166/wcc.2023.494.
]Search in Google Scholar
[
Aldiansyah S, Wardani F, (2024), Assessment of resampling methods on performance of landslide susceptibility predictions using machine learning in Kendari City, Indonesia. Water Practice & Technology, 19, (1):52–81. doi: 10.2166/wpt.2024.002.
]Search in Google Scholar
[
Aldiansyah S, Wahid KA, Ningsih DSW, (2022), Pemetaan Wilayah Kebakaran Hutan dan Lahan Menggunakan Citra Modis (Studi Kasus: Provinsi Sulawesi Tenggara. Jurnal Geosaintek, 8, (1): 141–150. doi: 10.12962/j25023659.v8i1.12019.
]Search in Google Scholar
[
Allouche O, Tsoar A, Kadmon R, (2006), Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS. Journal of Applied Ecology, 43, (6): 1223–1232. doi: 10.1111/j.1365-2664.2006.01214.x.
]Search in Google Scholar
[
Amalina P, Prasetyo LB, Rushayati SB, (2016), Forest Fire Vulnerability Mapping in Way Kambas National Park. Procedia Environmental Sciences, 33:239–252. doi: 10.1016/j.proenv.2016.03.075.
]Search in Google Scholar
[
Babu KN, Gour R, Ayushi K, Ayyappan N, Parthasarathy N, (2023), Environmental drivers and spatial prediction of forest fires in the Western Ghats biodiversity hotspot, India: An ensemble machine learning approach. Forest Ecology and Management, 540, (121057). doi: 10.1016/j.foreco.2023.121057.
]Search in Google Scholar
[
Badan Nasional Penanggulangan Bencana, (2023), Data Informasi Bencana Indonesia. Badan Nasional Penanggulangan Bencana. Retrivied 21 May 2023, from https://dibi.bnpb.go.id/home/index2.
]Search in Google Scholar
[
Batista GE, Prati RC, Monard MC, (2004), A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD explorations newsletter, 6, (1):20–29. doi: 10.1145/1007730.1007735.
]Search in Google Scholar
[
Bjånes A, Fuente R, Mena P, (2021), A deep learning ensemble model for wildfire susceptibility mapping. Ecological Informatics, 65, (101397). doi: 10.1016/j.ecoinf.2021.101397.
]Search in Google Scholar
[
Bonaccorso G, (2017), Machine learning algorithms. Packt Publishing Ltd,.
]Search in Google Scholar
[
Breiman L, (2001), Random Forests. Machine Learning, 45:5–32.
]Search in Google Scholar
[
Breiman L, (2017), Classification and regression trees. Routledge,.
]Search in Google Scholar
[
Bustillo Sánchez M, Tonini M, Mapelli A, Fiorucci P, (2021), Spatial Assessment of Wildfires Susceptibility in Santa Cruz (Bolivia. Using Random Forest. Geosciences, 11, (5). doi:10.3390/geosciences11050224.
]Search in Google Scholar
[
Castro R, Chuvieco E, (1998), Modeling forest fire danger from geographic information systems. Geocarto International, 13, (1):15–23. doi: 10.1080/10106049809354624.
]Search in Google Scholar
[
Chang CC, Lin CJ, (2011), LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST, 2, (3):1–27. doi: 10.1145/1961189.1961199.
]Search in Google Scholar
[
Chicas SD, Østergaard Nielsen J, Valdez MC, Chen CF, (2022), Modelling wildfire susceptibility in Belize’s ecosystems and protected areas using machine learning and knowledge-based methods. Geocarto International, pp. 1–24. doi: 10.1080/10106049.2022.2102231.
]Search in Google Scholar
[
CIESIN, (2018), Center for International Earth Science Information Network, Gridded Population of the World, Version 4 (GPWv4): Population Density. Revision, 11. doi: 10.7927/H49C6VHW. SEDAC).
]Search in Google Scholar
[
Cieslak DA, Chawla NV, (2008), Start globally, optimize locally, predict globally: Improving performance on imbalanced data. In: 2008 Eighth IEEE International Conference on Data Mining. IEEE, Pisa„ pp. 143–152.
]Search in Google Scholar
[
Dai FC, Lee CF, (2001), Frequency–volume relation and prediction of rainfall-induced landslides. Engineering Geology, 59, (3-4):253–266. doi: 10.1016/S0013-7952(00)00077-6.
]Search in Google Scholar
[
Dieterle FJ, (2003), Multianalyte quantifications by means of integration of artificial neural networks, genetic algorithms and chemometrics for time-resolved analytical data.
]Search in Google Scholar
[
Ebrahimy H, Rasuly A, Mokhtari D, (2017), Development of a Web GIS System Based on the MaxEnt Approach for Wildfire Management: A Case Study of East Azerbaijan. Ecopersia, 5.
]Search in Google Scholar
[
Environmental Protection Agency, (2023), Disaster-Resilient Design Concepts. Available at: https://www.epa.gov/system/files/documents/2023-03/Disaster-ResilientDesignConcepts_Feb2023v-6_Final.pdf.
]Search in Google Scholar
[
Eslami R, Azarnoush M, Kialashki A, Kazemzadeh F, (2021), GISbased forest fire susceptibility assessment by random forest, artificial neural network and logistic regression methods. Journal of Tropical Forest Science, 33, (2):173–184. doi:10.26525/jtfs2021.33.2.173.
]Search in Google Scholar
[
Ferentinou M, Chalkias C, (2013), Mapping mass movement susceptibility across Greece with GIS, ANN and statistical methods. Springer, Berlin, Heidelberg.
]Search in Google Scholar
[
Fick SE, Hijmans RJ, (2017), WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37, (12):4302–4315. doi: 10.1002/joc.5086.
]Search in Google Scholar
[
Food and Agriculture Organization of the United Nations, (2020), Global Forest Resources Assessment 2020 Main Report. In: Food and Agriculture Organization of the United Nations. doi:10.4060/ca9825en.
]Search in Google Scholar
[
Food and Agriculture Organization of the United Nations, (2021), Tracking Forest Fires from Above. Food and Agriculture Organization of the United Nations. Avaliable at: https://www.fao.org/national-forest-monitoring/news/detail/en/c/1403504/.
]Search in Google Scholar
[
Fotovatikhah F, Herrera M, Shamshirband S, Chau KW, Faizollahzadeh Ardabili S, Piran MJ, (2018), Survey of computational intelligence as basis to big flood management: Challenges, research directions and future work. Engineering Applications of Computational Fluid Mechanics, 12, (1):411–437. doi:10.1080/19942060.2018.1448896.
]Search in Google Scholar
[
Fox J, (2002), Bootstrapping regression models. The Annals of Statistics, 9. doi: 10.1214/aos/1176345638.
]Search in Google Scholar
[
Friedman JH, (1991), Multivariate adaptive regression splines. The annals of statistics, 19, (1):1–67. doi: 10.1214/aos/1176347963.
]Search in Google Scholar
[
Fu M, Fan T, Ding ZA, Salih SQ, Al-Ansari N, Yaseen ZM, (2020), Deep learning data-intelligence model based on adjusted forecasting window scale: application in daily streamflow simulation. IEEE Access, 8:32632–32651. doi: 10.1109/ACCESS.2020.2974406.
]Search in Google Scholar
[
Galar M, Fernandez A, Barrenechea E, Bustince H, Herrera F, (2011), A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews, 42, (4):463–484. doi: 10.1109/TSMCC.2011.2161285.
]Search in 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. doi: 10.3390/sym12040604.
]Search in Google Scholar
[
Ghorbanzadeh O, Valizadeh Kamran K, Blaschke T, Aryal J, Naboureh A, Einali J, Bian J, (2019), Spatial Prediction of Wildfire Susceptibility Using Field Survey GPS Data and Machine Learning Approaches. Fire, 2, (3). doi: 10.3390/fire2030043.
]Search in Google Scholar
[
Hai T, Theruvil Sayed B, Majdi A, Zhou J, Sagban R, Band SS, Mosavi A, (2023), An integrated GIS-based multivariate adaptive regression splines-cat swarm optimization for improving the accuracy of wildfire susceptibility mapping. Geocarto International, 2167005. doi: 10.1080/10106049.2023.2167005.
]Search in Google Scholar
[
Hair JF, Black WC, Babin BJ, Anderson RE, Tatham RL, (1998), Multivariate data analysis, volume 5. Prentice Hall,, Upper Saddle River, NJ.
]Search in Google Scholar
[
Hastie T, Tibshirani R, Buja A, (1994), Flexible discriminant analysis by optimal scoring. Journal of The American Statistical Association, 89. doi: 10.1080/01621459.1994.10476866.
]Search in Google Scholar
[
Hastie T, Tibshirani R, Friedman J, (2009), Random forests, The elements of statistical learning. Springer,, New York, NY.
]Search in Google Scholar
[
Hawbaker TJ, Henne PD, Vanderhoof MK, Carlson AR, Mockrin MH, Radeloff VC, (2023), Changes in wildfire occurrence and risk to homes from 1990 through 2019 in the Southern Rocky Mountains, USA. Ecosphere, 14, (2). doi: 10.1002/ecs2.4403.
]Search in 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. Arabian Journal of Geosciences, 10:1–14. doi: 10.1007/s12517-017-2905-4.
]Search in 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. Ecological Indicators, 101:878–891. doi: 10.1016/j.ecolind.2019.01.056.
]Search in Google Scholar
[
Hosseini M, Lim S, (2022), Gene expression programming and data mining methods for bushfire susceptibility mapping in New South Wales, Australia. Natural Hazards, 113. doi:10.1007/s11069-022-05350-7.
]Search in Google Scholar
[
Hou E, Rudgers JA, Collins SL, Litvak ME, White CS, Moore DI, Luo Y, (2021), Sensitivity of soil organic matter to climate and fire in a desert grassland. Biogeochemistry, 156:59–74. doi:10.1007/s10533-020-00713-3.
]Search in Google Scholar
[
Iban MC, Sekertekin A, (2022), Machine learning based wildfire susceptibility mapping using remotely sensed fire data and GIS: A case study of Adana and Mersin provinces, Turkey. Ecological Informatics, 69:101647. doi: 10.1016/j.ecoinf.2022.101647.
]Search in Google Scholar
[
Jaafari A, Zenner EK, Panahi M, Shahabi H, (2019), Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability. Agricultural and Forest Meteorology, 266: 198–207. doi: 10.1016/j.agrformet.2018.12.015.
]Search in Google Scholar
[
Jain P, Coogan SC, Subramanian SG, Crowley M, Taylor S, Flannigan MD, (2020), A review of machine learning applications in wildfire science and management. Environmental Reviews, 28, (4):478–505. doi: 10.1139/er-2020-0019.
]Search in Google Scholar
[
Janizadeh S, Bateni SM, Jun C, Im J, Pai HT, Band SS, Mosavi A, (2023), Combination four different ensemble algorithms with the generalized linear model (GLM) for predicting forest fire susceptibility. Geomatics, Natural Hazards and Risk, 14, (1). doi: 10.1080/19475705.2023.2206512.
]Search in Google Scholar
[
Kalantar B, Ueda N, Idrees MO, Janizadeh S, Ahmadi K, Shabani F, (2020), Forest Fire Susceptibility Prediction Based on Machine Learning Models with Resampling Algorithms on Remote Sensing Data. Remote Sensing, 12, (22). doi: 10.3390/rs12223682.
]Search in Google Scholar
[
Karra K, Kontgis C, Statman-Weil Z, Mazzariello JC, Mathis M, Brumby SP, (2021), Global land use/land cover with Sentinel 2 and deep learning. In: 2021 IEEE international geoscience and remote sensing symposium IGARSS. IEEE,, pp. 4704–4707. doi: 10.1109/IGARSS47720.2021.9553499.
]Search in Google Scholar
[
Kasin I, Blanck YL, Storaunet KO, Rolstad J, Ohlson M, (2013), The charcoal record in peat and mineral soil across a boreal landscape and possible linkages to climate change and recent fire history. The Holocene, 23, (7):1052–1065. doi: 10.1177/0959683613479678.
]Search in Google Scholar
[
Kato A, Thau D, Hudak AT, Meigs GW, Moskal LM, (2020), Quantifying fire trends in boreal forests with Landsat time series and self-organized criticality. Remote Sensing of Environment, 237:111525. doi: 10.1016/j.rse.2019.111525.
]Search in Google Scholar
[
Kavzoglu T, Mather PM, (2003), The use of backpropagating artificial neural networks in land cover classification. International Journal of Remote Sensing, 24, (23):4907–4938. doi: 10.1080/0143116031000114851.
]Search in Google Scholar
[
Kim SJ, Lim CH, Kim GS, Lee J, Geiger T, Rahmati O, Son Y, Lee WK, (2019), Multi-temporal analysis of forest fire probability using socio-economic and environmental variables. Remote Sensing, 11, (1):86. doi: 10.3390/rs11010086.
]Search in Google Scholar
[
Kohonen T, (1995), Learning vector quantization; self-organizing maps. Springer,, Berlin.
]Search in Google Scholar
[
Lacerda HC, Faria AL, Torres FT, Fonseca HP, Soares WO, Silva MA, (2022), Susceptibility to wildfire in a conservation unit located in the transition region of Cerrado and Atlantic Forest Biomes, Brazil. Ciência Florestal, 32:451–473. doi: 10.5902/1980509864171.
]Search in Google Scholar
[
Mabdeh AN, Al-Fugara A, Khedher KM, Mabdeh M, Al-Shabeeb AR, Al-Adamat R, (2022), Forest Fire Susceptibility Assessment and Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Evolutionary Algorithms. Sustainability, 14, (15). doi: 10.3390/su14159446.
]Search in Google Scholar
[
Malik A, Rao MR, Puppala N, Koouri P, Thota VA, Liu Q, Chiao S, Gao J, (2021), Data-Driven Wildfire Risk Prediction in Northern California. Atmosphere, 12, (1). doi: 10.3390/atmos12010109.
]Search in Google Scholar
[
McCullagh P, (2019), Generalized linear models. Routledge, . Meira-Castro A, Shakesby RA, Espinha Marques J, Doerr SH, Meixedo JP, Teixeira J, Chaminé HI, (2015), Effects of prescribed fire on surface soil in a Pinus pinaster plantation, northern Portugal. Environmental Earth Sciences, 73: 3011–3018. doi: 10.1007/s12665-014-3516-y.
]Search in Google Scholar
[
Moayedi H, Khasmakhi MA, (2023), Wildfire susceptibility mapping using two empowered machine learning algorithms. Stochastic Environmental Research and Risk Assessment, 37, (1):49–72. doi: 10.1007/s00477-022-02273-4.
]Search in Google Scholar
[
Mohajane M, Costache R, Karimi F, Bao Pham Q, Essahlaoui A, Nguyen H, Laneve G, Oudija F, (2021), Application of Remote Sensing and Machine Learning Algorithms for Forest Fire Mapping in A Mediterranean Area. Ecological Indicators, 129:107869. doi: 10.1016/j.ecolind.2021.107869.
]Search in Google Scholar
[
Mukaka MM, (2012), A guide to appropriate use of correlation coefficient in medical research. Malawi Medical Journal, 24, (3):69–71.
]Search in Google Scholar
[
Naghibi SA, Pourghasemi HR, (2015), A comparative assessment between three machine learning models and their performance comparison by bivariate and multivariate statistical methods in groundwater potential mapping. Water Resources Management, 29:5217–5236. doi: 10.1007/s11269-015-1114-8.
]Search in Google Scholar
[
Naimi B, Araújo MB, (2016), sdm: a reproducible and extensible R platform for species distribution modelling. Ecography, 39, (4):368–375. doi: 10.1111/ecog.01881.
]Search in Google Scholar
[
Nhongo EJ, Fontana DC, Guasselli LA, Bremm C, (2019), Probabilistic modelling of wildfire occurrence based on logistic regression, Niassa Reserve, Mozambique. Geomatics, Natural Hazards and Risk, 10. doi: 10.1080/19475705.2019.1615559.
]Search in Google Scholar
[
Nunes AN, Figueiredo A, Pinto CD, Lourenço L, (2023), An Evaluation of Wildfire Vulnerability in the Wildland–Urban Interfaces of Central Portugal Using the Analytic Network Process. Fire, 6, (5):194. doi: 10.3390/fire6050194.
]Search in Google Scholar
[
Nur A, Kim Y, Lee J, Lee CW, (2023), Spatial Prediction of Wildfire Susceptibility Using Hybrid Machine Learning Models Based on Support Vector Regression in Sydney, Australia. Remote Sensing, 15, (3). doi: 10.3390/rs15030760.
]Search in Google Scholar
[
Nur AS, Kim YJ, Lee CW, (2022), Creation of Wildfire Susceptibility Maps in Plumas National Forest Using InSAR Coherence, Deep Learning, and Metaheuristic Optimization Approaches. Remote Sensing, 14, (17). doi: 10.3390/rs14174416.
]Search in Google Scholar
[
Nurhayati AD, Saharjo BH, Sundawati L, Syartinilia S, Cochrane MA, (2021), Forest and Peatland Fire Dynamics in South Sumatra Province. Forest and Society, 5, (2): 591–603. doi: 10.24259/fs.v5i2.14435.
]Search in Google Scholar
[
Oliveira S, Gonçalves A, Zêzere JL, (2021), Reassessing wildfire susceptibility and hazard for mainland Portugal. Science of The Total Environment, 762, (143121). doi: 10.1016/j.scitotenv.2020.143121.
]Search in Google Scholar
[
Paudel J, (2023), Natural Disasters and Economic Inequality Insights from Wildfires Across the Globe. No. 24; Issue February). Retrivied 21 May 2023, from https://doi.org/10.35188/UNU-WIDER/2023/332-1 Typescript.
]Search in Google Scholar
[
Peel MC, Finlayson BL, McMahon TA, (2007), Updated world map of the Köppen-Geiger climate classification. Hydrology and Earth System Sciences, 11.
]Search in Google Scholar
[
Pham BT, Jaafari A, Avand M, Al-Ansari N, Dinh Du T, Yen HP, Phong TV, Nguyen DH, Le HV, Mafi-Gholami D, Prakash I, Thi Thuy H, Tuyen TT, (2020), Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction. Symmetry, 12, (6). doi: 10.3390/sym12061022.
]Search in Google Scholar
[
Piao Y, Lee D, Park S, Kim HG, Jin Y, (2022), Forest fire susceptibility assessment using google earth engine in Gangwon-do, Republic of Korea. Geomatics, Natural Hazards and Risk, 13, (1):432–450. doi: 10.1080/19475705.2022.2030808.
]Search in Google Scholar
[
Pourghasemi HR, (2016), GIS-based forest fire susceptibility mapping in Iran: a comparison between evidential belief function and binary logistic regression models. Scandinavian Journal of Forest Research, 31, (1):80–98. doi: 10.1080/02827581.2015.1052750.
]Search in Google Scholar
[
Prayoga MB, Koestoer RH, (2021), Improving Forest Fire Mitigation in Indonesia: A Lesson from Canada. Jurnal Wilayah Dan Lingkungan, 9, (3):293–305. doi: 10.14710/jwl.9.3.293-305.
]Search in Google Scholar
[
Price I, (2000), Research Methods and Statistics PESS202 Lecture and Commentary Notes. University of New England,, Armidale.
]Search in Google Scholar
[
Robinne FN, Parisien MA, Flannigan M, (2016), Anthropogenic influence on wildfire activity in Alberta, Canada. International Journal of Wildland Fire, 25. doi: 10.1071/WF16058.
]Search in Google Scholar
[
Sachdeva S, Bhatia T, Verma AK, (2018), GIS-based evolutionary optimized Gradient Boosted Decision Trees for forest fire susceptibility mapping. Natural Hazards, 92:1399–1418. doi:10.1007/s11069-018-3256-5.
]Search in Google Scholar
[
Salavati G, Saniei E, Ghaderpour E, Hassan QK, (2022), Wildfire Risk Forecasting Using Weights of Evidence and Statistical Index Models. Sustainability, 14, (7). doi: 10.3390/su14073881.
]Search in Google Scholar
[
San Miguel J, Chuvieco E, Handmer J, Moffat A, Montiel-Molina C, Sandahl L, (2017), Climatological Risk: Wildfires. In: Poljanšek K, Ferrer M, Groeve T, Clark I, (eds.) Science for Disaster Risk Management 2017: Knowing Better and Losing Less. European Union,, pp. 294–335. doi: 10.2788/842809.
]Search in Google Scholar
[
Santana RO, Delgado RC, Schiavetti A, (2021), Modeling susceptibility to forest fires in the Central Corridor of the Atlantic Forest using the frequency ratio method. Journal of Environmental Management, 296:113343. doi: 10.1016/j.jenvman.2021.113343.
]Search in Google Scholar
[
Sarwono J, (2006), Metode Penelitian Kuantitatif dan Kualitatif. Yogyakarta. Graha Ilmu, .
]Search in Google Scholar
[
Sazib N, Bolten JD, Mladenova IE, (2021), Leveraging NASA Soil Moisture Active Passive for Assessing Fire Susceptibility and Potential Impacts over Australia and California. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15:779–787. doi: 10.1109/JSTARS.2021.3136756.
]Search in Google Scholar
[
Schueremans L, Gemert D, (2005), Benefit of splines and neural networks in simulation based structural reliability analysis. Structural Safety, 27, (3):246–261. doi: 10.1016/j.strusafe.2004.11.001.
]Search in Google Scholar
[
Shahfahad T, S D, T N, W M, Rihan M, Rahman A, (2022), Forest fire susceptibility mapping by integrating remote sensing and machine learning algorithms. Advances in Remote Sensing for Forest Monitoring, pp. 179–195. doi: 10.1002/9781119788157.ch9.
]Search in Google Scholar
[
Shao Y, Feng Z, Sun L, Yang X, Li Y, Xu B, Chen Y, (2022), Mapping China’s Forest Fire Risks with Machine Learning. Forests, 13, (6):1–21. doi: 10.3390/f13060856.
]Search in Google Scholar
[
Sharma LK, Gupta R, Fatima N, (2022), Assessing the predictive efficacy of six machine learning algorithms for the susceptibility of Indian forests to fire. International Journal of Wildland Fire, 31, (8):735–758. doi: 10.1071/WF22016.
]Search in Google Scholar
[
Sharma S, Ochsner TE, Twidwell D, Carlson JD, Krueger ES, Engle DM, Fuhlendorf SD, (2018), Nondestructive estimation of standing crop and fuel moisture content in tallgrass prairie. Rangeland ecology & management, 71, (3):356–362. doi: 10.1016/j.rama.2018.01.001.
]Search in Google Scholar
[
Shi K, Touge Y, (2023), Identifying the shift in global wildfire weather conditions over the past four decades: an analysis based on change-points and long-term trends. Geoscience Letters, 10, (1):1–16. doi: 10.1186/s40562-022-00255-6.
]Search in Google Scholar
[
Shmuel A, Heifetz E, (2022), Global Wildfire Susceptibility Mapping Based on Machine Learning Models. Forests, 13. doi: 10.3390/f13071050.
]Search in Google Scholar
[
Sugiarto DP, (2013), Strategi Pengendalian Kebakaran Hutan Dan Lahan di Taman Nasional Rawa Aopa Watumohai Dengan Pemanfaatan Pemodelan Data Spasial. Institut Pertanian Bogor,, Bogor.
]Search in Google Scholar
[
Sulova A, Jokar Arsanjani J, (2020), Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine. Remote Sensing, 13, (1). doi: 10.3390/rs13010010.
]Search in Google Scholar
[
Syifa M, Panahi M, Lee CW, (2020), Mapping of Post-Wildfire Burned Area Using a Hybrid Algorithm and Satellite Data: The Case of the Camp Fire Wildfire in California, USA. Remote Sensing, 12, (4):623. doi: 10.3390/rs12040623.
]Search in Google Scholar
[
Tan C, Feng Z, (2023), Mapping Forest Fire Risk Zones Using Machine Learning Algorithms in Hunan Province, China. Sustainability, 15, (7). doi: 10.3390/su15076292.
]Search in Google Scholar
[
Tavakkoli Piralilou S, Einali G, Ghorbanzadeh O, Nachappa TG, Gholamnia K, Blaschke T, Ghamisi P, (2022), A Google Earth Engine Approach for Wildfire Susceptibility Prediction Fusion with Remote Sensing Data of Different Spatial Resolutions. Remote Sensing, 14, (3). doi: 10.3390/rs14030672.
]Search in Google Scholar
[
Tien Bui D, Le KT, 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 Sensing, 8, (4). doi: 10.3390/rs8040347.
]Search in Google Scholar
[
Tonini M, D’Andrea M, Biondi G, Degli Esposti S, 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. doi: 10.3390/geosciences10030105.
]Search in Google Scholar
[
Tonini M, Bustillo Sanchez M, Mapelli A, Fiorucci P, (2021), Susceptibility wildfire assessment in Bolivia (Santa Cruz): an approach based on Random Forest ensemble learning algorithm. In: EGU General Assembly Conference Abstracts, pp. 21–5653. doi: 10.5194/egusphere-egu21-5653.
]Search in Google Scholar
[
Trucchia A, Izadgoshasb H, Isnardi S, Fiorucci P, Tonini M, (2022), Machine-Learning Applications in Geosciences: Comparison of Different Algorithms and Vegetation Classes’ Importance Ranking in Wildfire Susceptibility. Geosciences, 12, (11). doi: 10.3390/geosciences12110424.
]Search in Google Scholar
[
United Nations Environment Programme, (2022), Spreading like Wildfire-Threat of Extraordinary Landscape Fires. Engineer, 294, (ue 7783)). doi: 10.1038/news000413-8.
]Search in Google Scholar
[
West AM, Evangelista PH, Jarnevich CS, Kumar S, Swallow A, Luizza MW, Chignell SM, (2017), Using multi-date satellite imagery to monitor invasive grass species distribution in postwildfire landscapes: An iterative, adaptable approach that employs open-source data and software. International Journal of Applied Earth Observation and Geoinformation, 59:135–146.
]Search in Google Scholar
[
Winter JC, Gosling SD, Potter J, (2016), Comparing the Pearson and Spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data. Psychological Methods, 21, (3). doi: 10.1037/met0000079.
]Search in Google Scholar
[
Xu H, Deng Y, (2017), Dependent evidence combination based on shearman coefficient and pearson coefficient. IEEE Access, 6: 11634–11640. doi: 10.1109/ACCESS.2017.2783320.
]Search in Google Scholar
[
Zhang G, Wang M, Liu K, (2021), Deep neural networks for global wildfire susceptibility modelling. Ecological Indicators, 127, (107735). doi: 10.1016/j.ecolind.2021.107735.
]Search in Google Scholar
[
Zhang Y, Lim S, (2019), Drivers of Wildfire Occurrence Patterns in the Inland Riverine Environment of New South Wales, Australia. Forests, 10, (6). doi: 10.3390/f10060524.
]Search in Google Scholar
[
Zheng Y, Tang L, Wang H, (2021), An improved approach for monitoring urban built-up areas by combining NPP-VIIRS nighttime light, NDVI, NDWI, and NDBI. Journal of Cleaner Production, 328:129488. doi: 10.1016/j.jclepro.2021.129488.
]Search in Google Scholar
