Abstract
This study primarily aims to produce forest fire susceptibility maps for the Manavgat district of Antalya province in Turkey using different machine learning (ML) techniques. Forest fire inventory data were obtained from the General Directorate of Forestry. The inventory data comprise a total of 545 forest fire ignition points during the years 2013–2021. For model training and validation, 70% and 30% of these points were used, respectively. Average annual temperature, average annual rainfall, aspect, distance to rivers, elevation, distance to settlements, forest type, distance to roads, land cover, plan curvature, slope, solar radiation, tree cover density, topographic wetness index, and wind effect parameters were used in the study. Multicollinearity analysis of these 15 factors showed that they are independent of each other. Tree-based ML models, namely, eXtreme gradient boosting (XGBoost), random forest, and gradient boosting machine, as well as artificial neural networks (ANN) were used to produce forest fire susceptibility maps. The metrics of overall accuracy, precision, recall, F1 score and area under the receiver operating characteristic curve (AU-ROC) were used to evaluate the performance of the ML models. Based on our results, the XGBoost model revealed the most appropriate susceptibility map that could be used for fire prevention measures.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Data will be made available on request.
References
Abdo HG, Almohamad H, Al Dughairi AA, Al-Mutiry M (2022) GIS-based frequency ratio and analytic hierarchy process for forest fire susceptibility mapping in the western region of Syria. Sustainability 14(8):4668. https://doi.org/10.3390/su14084668
Abedi Gheshlaghi HA, Feizizadeh B, Blaschke T (2020) GIS-based forest fire risk mapping using the analytical network process and fuzzy logic. J Environ Plan Manag 63(3):481–499. https://doi.org/10.1080/09640568.2019.1594726
Achu AL, Thomas J, Aju CD, Gopinath G, Kumar S, Reghunath R (2021) Machine-learning modelling of fire susceptibility in a forest-agriculture mosaic landscape of southern India. Ecol Inf 64:101348. https://doi.org/10.1016/j.ecoinf.2021.101348
Adab H, Kanniah KD, Solaimani K (2013) Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Nat Hazards 65(3):1723–1743. https://doi.org/10.1007/s11069-012-0450-8
Aditian A, Kubota T, Shinohara Y (2018) Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon. Indonesia Geomorphology 318:101–111. https://doi.org/10.1016/j.geomorph.2018.06.006
Akay AE, Şahin H (2019) Forest fire risk mapping by using GIS techniques and AHP method: a case study in Bodrum (Turkey). Eur J Forest Eng 5(1):25–35. https://doi.org/10.33904/ejfe.579075
Akinci H (2022) Assessment of rainfall-induced landslide susceptibility in Artvin, Turkey using machine learning techniques. J Afr Earth Sci 191:104535. https://doi.org/10.1016/j.jafrearsci.2022.104535
Akinci H, Yavuz Ozalp A (2021) Landslide susceptibility mapping and hazard assessment in Artvin (Turkey) using frequency ratio and modified information value model. Acta Geophys 69(3):725–745. https://doi.org/10.1007/s11600-021-00577-7
Akinci H, Zeybek M (2021) Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey. Nat Hazards 108(2):1515–1543. https://doi.org/10.1007/s11069-021-04743-4
Al Saim AA, Aly MH (2022) Machine learning for modeling wildfire susceptibility at the state level: an example from Arkansas. USA Geographies 2(1):31–47. https://doi.org/10.3390/geographies2010004
Anderson HE (1982) Aids to determining fuel models for estimating fire behavior. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, Ogden, Utah
Arca D, Hacısalihoğlu M, Kutoğlu ŞH (2020) Producing forest fire susceptibility map via multi-criteria decision analysis and frequency ratio methods. Nat Hazards 104(1):73–89. https://doi.org/10.1007/s11069-020-04158-7
Asori M, Emmanuel D, Dumedah G (2020) Wildfire hazard and risk modelling in the Northern regions of Ghana using GIS-based Multi-Criteria decision making analysis. Environ Earth Sci 10(11):11–28
Bilgili E (2003) Stand development and fire behavior. For Ecol Manag 179(1–3):333–339. https://doi.org/10.1016/S0378-1127(02)00550-9
Bjånes A, De La Fuente R, Mena P (2021) A deep learning ensemble model for wildfire susceptibility mapping. Ecol Inf 65:101397. https://doi.org/10.1016/j.ecoinf.2021.101397
Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324
Butry DT, Prestemon JP (2005) Spatiotemporal wild land arson crime functions. Selected paper presented at the American Agricultural Economics Association Annual Meeting, Providence, Rhode Island, July 24–27. American Agricultural Economics Association, 28 p
Calp MH, Kose U (2020) Estimation of burned areas in forest fires using artificial neural networks. ing Solidar 16(3):1–22. https://doi.org/10.16925/2357-6014.2020.03.08
Can R, Kocaman S, Gokceoglu C (2021) A comprehensive assessment of XGBoost algorithm for landslide susceptibility mapping in the upper basin of Ataturk dam, Turkey. Appl Sci 11(11):4993. https://doi.org/10.3390/app11114993
Chen T, Guestrin C (2016) XGBoost. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining—KDD’16; New York. ACM Press, pp. 785–794
Chen W, Peng J, Hong H, Shahabi H, Pradhan B, Liu J, Zhu AX, Pei X, Duan Z (2018) Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. Sci Total Environ 626:1121–1135. https://doi.org/10.1016/j.scitotenv.2018.01.124
Chen T, Zhu L, Niu R-q, Trinder CJ, Peng L, Lei T (2020) Mapping landslide susceptibility at the three Gorges Reservoir, China, using gradient boosting decision tree, random forest and information value models. J Mt Sci 17(3):670–685. https://doi.org/10.1007/s11629-019-5839-3
Chen T, He T (2022) xgboost: eXtreme Gradient Boosting. https://cran.r-project.org/web/packages/xgboost/vignettes/xgboost.pdf (accessed: 24.07.2021)
Chuvieco E, Cocero D, Riaño D, Martin P, Martínez-Vega J, de la Riva J, Pérez F (2004) Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sens Environ 92(3):322–331. https://doi.org/10.1016/j.rse.2004.01.019
Chuvieco E, Congalton RG (1988) Geocarto Int 3(4):41–53. https://doi.org/10.1080/10106048809354180. Mapping and inventory of forest fires from digital processing of TM data
Çanakçıoğlu H (1993) Forest conservation. Istanbul University Faculty of forestry press, No 411, Istanbul, 633 p. (in Turkish)
Çoban HO, Erdin C (2020) Forest fire risk assessment using GIS and AHP integration in Bucak Forest Enterprise, Turkey. Appl Ecol Environ Res 18(1):1567–1583. https://doi.org/10.15666/aeer/1801_15671583
de 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. J Environ Manage 296:113343. https://doi.org/10.1016/j.jenvman.2021.113343
El Ouiqary A, Kheddioui EM, Smiej MF (2021) Estimation of the global horizontal solar irradiation GHI for the moroccan national territory from meteorological satellite images of the Second Generation Meteosat series MSG. Eur J Mol Clin Med 8(3):2814–2826
Eskandari S, Miesel JR (2017) Comparison of the fuzzy AHP method, the spatial correlation method, and the Dong model to predict the fire high-risk areas in Hyrcanian forests of Iran. Natl Hazards Risk 8(2):933–949. https://doi.org/10.1080/19475705.2017.1289249
Faramarzi H, Hosseini SM, Pourghasemi HR, Farnaghi M (2021) Forest fire spatial modelling using ordered weighted averaging multi-criteria evaluation. J For Sci 67(2):87–100. https://doi.org/10.17221/50/2020-JFS
Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Statist 29(5):1189–1232. https://doi.org/10.1214/aos/1013203451
Ghorbanzadeh O, Blaschke T, Gholamnia K, Aryal J (2019) Forest fire susceptibility and risk mapping using social/infrastructural vulnerability and environmental variables. Fire 2(3):50. https://doi.org/10.3390/fire2030050
Gholamnia K, Nachappa G, Ghorbanzadeh T, Blaschke O, T (2020) Comparisons of diverse machine learning approaches for wildfire susceptibility mapping. Symmetry 12(4):604. https://doi.org/10.3390/sym12040604
Guo F, Su Z, Wang G, Sun L, Lin F, Liu A (2016) Wildfire ignition in the forests of southeast China: identifying drivers and spatial distribution to predict wildfire likelihood. Appl Geogr 66:12–21. https://doi.org/10.1016/j.apgeog.2015.11.014
Güngöroğlu C (2017) Determination of forest fire risk with fuzzy analytic hierarchy process and its mapping with the application of GIS: the case of Turkey/Çakırlar. Hum Ecol Risk Assess 23(2):388–406. https://doi.org/10.1080/10807039.2016.1255136
He Q, Jiang Z, Wang M, Liu K (2021) Landslide and wildfire susceptibility assessment in Southeast Asia using ensemble machine learning methods. Remote Sens 13(8):1572. https://doi.org/10.3390/rs13081572
Hernandez-Leal PA, Arbelo M, Gonzalez-Calvo A (2006) Fire risk assessment using satellite data. Adv Space Res 37(4):741–746. https://doi.org/10.1016/j.asr.2004.12.053
Hong H, Tsangaratos P, Ilia I, Liu J, Zhu AX, Xu C (2018) The case of Dayu County China Sci Total Environ 630:1044–1056. https://doi.org/10.1016/j.scitotenv.2018.02.278. Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models
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. Turk Ecol Inf 69:101647. https://doi.org/10.1016/j.ecoinf.2022.101647
Ibrahem Ahmed Osman A, Najah Ahmed A, Chow MF, Feng Huang Y, El-Shafie A (2021) Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia. Ain Shams Eng J 12(2):1545–1556. https://doi.org/10.1016/j.asej.2020.11.011
Jaafari A, Mafi-Gholami D, Thai Pham B, Tien Bui D (2019) Wildfire probability mapping: Bivariate vs. multivariate statistics. Remote Sens 11(6):618. https://doi.org/10.3390/rs11060618
Jaiswal RK, Mukherjee S, Raju KD, Saxena R (2002) Forest fire risk zone mapping from satellite imagery and GIS. Int J Appl Earth Obs Geoinf 4(1):1–10. https://doi.org/10.1016/S0303-2434(02)00006-5
James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning with applications in R. Springer-Verlag, New York
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 Sens 12(22):3682. https://doi.org/10.3390/rs12223682
Kopecký M, Macek M, Wild J (2021) Topographic wetness index calculation guidelines based on measured soil moisture and plant species composition. Sci Total Environ 757:143785. https://doi.org/10.1016/j.scitotenv.2020.143785
Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw 28(5):1–26. https://doi.org/10.18637/jss.v028.i05
Küçük Ö, Bilgili E, Durmaz BD, Sağlam B, Baysal İ (2009) The effect factors on transition from surface fire to crown fire. Kastamonu Univ J For Fac 9(2):80–85
Le HV, Hoang DA, Tran CT, Nguyen PQ, Tran VHT, Hoang ND, Amiri M, Ngo TPT, Nhu HV, Hoang TV, Bui T, D (2021) A new approach of deep neural computing for spatial prediction of wildfire danger at tropical climate areas. Ecol Inf 63:101300. https://doi.org/10.1016/j.ecoinf.2021.101300
Ma J, Lin G, Chen J, Yang L (2010) An improved topographic wetness index considering topographic position. 18th International Conference on Geoinformatics, pp. 1–4. https://doi.org/10.1109/GEOINFORMATICS.2010.5567607
Mohajane M, Costache R, Karimi F, Bao Pham QB, 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. Ecol Indic 129:107869. https://doi.org/10.1016/j.ecolind.2021.107869
Nhongo EJS, Fontana DC, Guasselli LA, Bremm C (2019) Probabilistic modelling of wildfire occurrence based on logistic regression, Niassa Reserve, Mozambique. Geomat Natl. Hazards Risk 10(1):1772–1792. https://doi.org/10.1080/19475705.2019.1615559
Noonan EK (2003) A coupled model approach for assessing fire hazard at point Reyes national seashore: FlamMap and GIS. In: Second international wildland fire ecology and fire manage. congress and fifth symposium on fire and forest meteorology, Orlando, FL. American Meteorological Society, pp. 127–128
Novkovic I, Markovic GB, Lukic D, Dragicevic S, Milosevic M, Djurdjic S, Samardzic I, Lezaic T, Tadic M (2021) GIS-based forest fire susceptibility zonation with IoT sensor network support, case study—nature Park Golija. Serbia Sens (Basel) 21(19):6520. https://doi.org/10.3390/s21196520
Oliveira S, Pereira JMC, San-Miguel-Ayanz J, Lourenço L (2014) Exploring the spatial patterns of fire density in southern Europe using geographically weighted regression. Appl Geogr 51:143–157. https://doi.org/10.1016/j.apgeog.2014.04.002
Pham BT, Jaafari A, Avand M, Al-Ansari N, Dinh Du T, Yen HPH, 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 12:1022. https://doi.org/10.3390/sym12061022
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. Geomat Natl Hazards Risk 13(1):432–450. https://doi.org/10.1080/19475705.2022.2030808
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. https://doi.org/10.1080/02827581.2015.1052750
Pourghasemi HR, Gayen A, Lasaponara R, John P, Tiefenbacher JP (2020) Application of learning vector quantization and different machine learning techniques to assessing forest fire influence factors and spatial modelling. Environ Res 184:109321. https://doi.org/10.1016/j.envres.2020.109321
Pourghasemi HR, Sadhasivam N, Amiri M, Eskandari S, Santosh M (2021) Landslide susceptibility assessment and mapping using state-of-the art machine learning techniques. Nat Hazards 108(1):1291–1316. https://doi.org/10.1007/s11069-021-04732-7
Pourtaghi ZS, Pourghasemi HR, Aretano R, Semeraro T (2016) Ecol Indic 64:72–84. https://doi.org/10.1016/j.ecolind.2015.12.030. Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques
Prasad VK, Badarinath KVS, Eaturu A (2008) Biophysical and anthropogenic controls of forest fires in the Deccan Plateau, India. J Environ Manage 86(1):1–13. https://doi.org/10.1016/j.jenvman.2006.11.017
Rajabi M, Alesheikh A, Chehreghan A, Gazmeh H (2013) An innovative method for forest fire risk zoning map using fuzzy inference system and GIS. Int J Sci Technol Res 2(12):57–64
San-Miguel-Ayanz J, Durrant T, Boca R, Maianti P, Libertá G, Artés-Vivancos T, Oom D, Branco A, de Rigo D, Ferrari D, Pfeiffer H, Grecchi R, Nuijten D (2022) Advance Report on Forest Fires in Europe, Middle East and North Africa 2021, JRC Technical Report. En: EUR. Publications Office of the European Union, Luxembourg, p. 31028. https://doi.org/10.2760/039729, JRC128678
Santana Neto VP, Vieira Leite R, Juste dos Santos V, do, Carmo Alves S, de Siqueira Castro J, Pereira Torres T, Calijuri FLucia (2022) M., Burning susceptibility modeling to reduce wildfire impacts: A GIS and multivariate statistics approach. Floresta Ambiente 29 (1), e20210078. https://doi.org/10.1590/2179-8087-FLORAM-2021-0078
Saglam B, Bilgili E, Dincdurmaz B, Kadiogulari AI, Kücük Ö (2008) Spatio-temporal analysis of forest fire risk and danger using LANDSAT imagery. Sens (Basel) 8(6):3970–3987. https://doi.org/10.3390/s8063970
Sari F (2021) Forest fire susceptibility mapping via multi-criteria decision analysis techniques for Mugla, Turkey: a comparative analysis of VIKOR and TOPSIS. For Ecol Manag 480:118644. https://doi.org/10.1016/j.foreco.2020.118644
Setiawan I, Mahmud AR, Mansor S, Shariff M, Nuruddin AR, A.A (2004) Malaysia Disaster Prev Manag 13(5):379–386. https://doi.org/10.1108/09653560410568507. GIS-grid-based and multi-criteria analysis for identifying and mapping peat swamp forest fire hazard in Pahang
Sivrikaya F, Küçük Ö (2022) Modeling forest fire risk based on GIS-based analytical hierarchy process and statistical analysis in Mediterranean region. Ecol Inf 68:101537. https://doi.org/10.1016/j.ecoinf.2021.101537
Tavakkoli Piralilou ST, 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 Sens 14(3):672. https://doi.org/10.3390/rs14030672
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 Hai Phong City Vietnam 8(4):8. https://doi.org/10.3390/rs8040347
Tien Bui D, Bui Q-T, Nguyen Q-P, Pradhan B, Nampak H, Trinh PT (2017) Agric For Meteorol 233:32–44. https://doi.org/10.1016/j.agrformet.2016.11.002. 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
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. https://doi.org/10.3390/geosciences10030105
Trucchia A, Meschi G, Fiorucci P, Gollini A, Negro D (2022) Defining wildfire susceptibility maps in Italy for understanding seasonal wildfire regimes at the national level. Fire 5(1):30. https://doi.org/10.3390/fire5010030
Tshering K, Thinley P, Shafapour Tehrany MS, Thinley U, Shabani F (2020) A comparison of the qualitative analytic hierarchy process and the quantitative frequency ratio techniques in predicting forest fire-prone areas in Bhutan using GIS. Forecasting 2(2):36–58. https://doi.org/10.3390/forecast2020003
URL1, ESA WorldCover – Map. https://viewer.esa-worldcover.org/worldcover/ (accessed: 21.04.2022)
URL2, EU-hydro – River network database. https://land.copernicus.eu/imagery-in-situ/eu-hydro/eu-hydro-river-network-database (accessed: 21.06.2022)
URL3, global solar. https://globalsolaratlas.info/download/turkey (accessed: 06.07.2022). Atlas
URL4, Copernicus forest Type (2018) https://land.copernicus.eu/pan-european/high-resolution-layers/forests/forest-type-1/status-maps/forest-type-2018 (accessed: 02.07.2021)
URL5, Copernicus tree cover density (2018) https://land.copernicus.eu/pan-european/high-resolution-layers/forests/tree-cover-density/status-maps/tree-cover-density-2018?tab=mapview (accessed: 02.07.2021)
USDA (2003) Influence of forest structure on wildfire behavior and the severity of its effects, An Overview. https://www.fs.fed.us/projects/hfi/docs/forest_structure_wildfire.pdf (accessed: 10.07.2022)
Üstüner M, Abdikan S, Bilgin G, Balik Şanli F (2020) Crop classification using light gradient boosting machines. Turk J Remote Sens GIS 1(2):97–105 (in Turkish)
Vasilakos C, Kalabokidis K, Hatzopoulos J, Matsinos I (2009) Identifying wildland fire ignition factors through sensitivity analysis of a neural network. Nat Hazards 50(1):125–143. https://doi.org/10.1007/s11069-008-9326-3
Verde JC, Zêzere JL (2010) Assessment and validation of wildfire susceptibility and hazard in Portugal. Nat Hazards Earth Syst 10(3):485–497. https://doi.org/10.5194/nhess-10-485-2010
Wang Z, Liu Q, Liu Y (2020) Mapping landslide susceptibility using machine learning algorithms and GIS: A case study in Shexian County, Anhui Province, China. Symmetry 12 (12), 1954. https://doi.org/10.3390/sym12121954
Wilson JP, Gallant JC (2000) Terrain analysis principles and applications. Wiley, Toronto, Canada
WWF (2022) Turkey. Ecological and Socio-Economic Effects of Major Forest Fires in the Mediterranean Region. https://wwftr.awsassets.panda.org/downloads/ormanyangnlarweb.pdf (accessed: 15.04.2022) (in Turkish)
Ye P, Yu B, Chen W, Liu K, Ye L (2022) Rainfall-induced landslide susceptibility mapping using machine learning algorithms and comparison of their performance in Hilly area of Fujian Province, China. Nat Hazards 113:965–995. https://doi.org/10.1007/s11069-022-05332-9
Youssef AM, Pourghasemi HR (2021) Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia. Geosci Front 12(2):639–655. https://doi.org/10.1016/j.gsf.2020.05.010
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. https://doi.org/10.1007/s13753-019-00233-1
Zhang G, Wang M, Liu K (2021) Deep neural networks for global wildfire susceptibility modelling. Ecol Indic 127:107735. https://doi.org/10.1016/j.ecolind.2021.107735
Zumbrunnen T, Pezzatti GB, Menéndez P, Bugmann H, Bürgi M, Conedera M (2011) Weather and human impacts on forest fires: 100 years of fire history in two climatic regions of Switzerland. For Ecol Manag 261(12):2188–2199. https://doi.org/10.1016/j.foreco.2010.10.009
Funding
This study was supported by the Scientific Research Projects Office of Artvin Çoruh University (AÇÜBAP) (Scientific Research Project No. 2022. F40.02.02).
Author information
Authors and Affiliations
Contributions
CRediT authorship contribution statement Hazan Alkan Akıncı: Conceptualization, Methodology, Writing – original draft. Halil Akıncı: Conceptualization, Methodology, Software, Validation, Visualization.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Additional information
Communicated by H. Babaie
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Akıncı, H.A., Akıncı, H. Machine learning based forest fire susceptibility assessment of Manavgat district (Antalya), Turkey. Earth Sci Inform 16, 397–414 (2023). https://doi.org/10.1007/s12145-023-00953-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12145-023-00953-5