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Satellite-based ensemble intelligent approach for predicting forest fire: a case of the Hyrcanian forest in Iran

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Abstract

A machine learning-based approach is applied to simulate and forecast forest fires in the Golestan province in Iran. A dataset for no-fire, medium confidence (MC) fire events, and high confidence (HC) fire events is constructed from MODIS-MOD14A2. Nine climate variables from NASA’s FLDAS are used as input variables, and 12 dates and 915 study points are considered. Three machine learning ensemble multi-label classifiers, gradient boosting (GBC), random forest (RFC), and extremely randomized tree (ETC), are used for forest fire simulation for the period 2000 to 2021, and ETC is found to be the most accurate classifier. Future fire projection for the near-future period of 2030 to 2050 is carried out with the ETC model, using CMIP6 EC-Earth3-SSP245 General Circulation Model (GCM) data. It is projected that MC forest fire occurrences will decrease, while HC forest fire occurrences will increase, and that the summer months, especially September, will be the most affected by fire.

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References

  • Abbaspour KC, Faramarzi M, Ghasemi SS, Yang H (2009) Assessing the impact of climate change on water resources in Iran. Water Resour Res 45(10). https://doi.org/10.1029/2008wr007615

  • Adab H (2017) Landfire hazard assessment in the Caspian Hyrcanian forest ecoregion with the long-term MODIS active fire data. Nat Hazards 87(3):1807–1825

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Adab H, Kanniah KD, Solaimani K (2021) Remote sensing-based operational modeling of fuel ignitability in Hyrcanian mixed forest, Iran. Nat Hazards 108(1):253–283

    Article  Google Scholar 

  • Asadollah SBHS, Sharafati A, Shahid S (2022) Application of ensemble machine learning model in downscaling and projecting climate variables over different climate regions in Iran. Environ Sci Pollut Res 1–20. https://doi.org/10.1007/s11356-021-16964-y

  • Avila-Flores D, Pompa-Garcia M, Antonio-Nemiga X, Rodriguez-Trejo DA, Vargas-Perez E, Santillan-Perez J (2010) Driving factors for forest fire occurrence in Durango State of Mexico: a geospatial perspective. Chin Geogra Sci 20(6):491–497

    Article  Google Scholar 

  • Balaji V, Taylor KE, Juckes M, Lawrence BN, Durack PJ, Lautenschlager M et al (2018) Requirements for a global data infrastructure in support of CMIP6. Geosci Model Dev 11(9):3659–3680

    Article  Google Scholar 

  • Barmpoutis P, Papaioannou P, Dimitropoulos K, Grammalidis N (2020) A review on early forest fire detection systems using optical remote sensing. Sensors 20(22):6442

    Article  Google Scholar 

  • Bhati BS, Rai CS (2020) Ensemble based approach for intrusion detection using extra tree classifier. In: Intelligent computing in engineering: select proceedings of RICE 2019. Springer Singapore, pp 213–220. https://doi.org/10.1007/978-981-15-2780-7_25

  • Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324

    Article  Google Scholar 

  • Bui DT, Van Le H, Hoang N-D (2018) GIS-based spatial prediction of tropical forest fire danger using a new hybrid machine learning method. Eco Inform 48:104–116

    Article  Google Scholar 

  • Bui DT, Hoang N-D, Samui P (2019) 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). J Environ Manag 237:476–487

    Article  Google Scholar 

  • Busico G, Giuditta E, Kazakis N, Colombani N (2019) A hybrid GIS and AHP approach for modelling actual and future forest fire risk under climate change accounting water resources attenuation role. Sustainability 11(24):7166

    Article  Google Scholar 

  • Chuvieco E, Congalton RG (1989) Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sens Environ 29(2):147–159

    Article  Google Scholar 

  • Cortes C, Jackel LD, Chiang WP (1994) Limits on learning machine accuracy imposed by data quality. Adv Neural Inf Proces Syst 7

  • Covey C, AchutaRao KM, Cubasch U, Jones P, Lambert SJ, Mann ME et al (2003) An overview of results from the Coupled Model Intercomparison Project. Glob Planet Chang 37(1–2):103–133

    Article  Google Scholar 

  • Diakakis M, Nikolopoulos EI, Mavroulis S, Vassilakis E, Korakaki E (2017) Observational evidence on the effects of mega-fires on the frequency of hydrogeomorphic hazards. The case of the Peloponnese fires of 2007 in Greece. Sci Total Environ 592:262–276

    Article  CAS  Google Scholar 

  • Dietterich TG (2000) An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach Learn 40(2):139–157

    Article  Google Scholar 

  • Duarte LG, Romera KS, Sabino M, Curado LFA, da Silva Palácios R, de Souza Nogueira J (2019) Dynamics of heat fluxes by BOWEN and MATMNXFLX and NOAH FLDAS products in the pantanal of Mato Grosso. Raega-O Espaço Geográfico Em Análise 46(3):7–19

    Article  Google Scholar 

  • Elsayed S, Gupta M, Chaudhary G, Taneja S, Gaur H, Gad M et al (2023) Interpretation the influence of hydrometeorological variables on soil temperature prediction using the potential of deep learning model. Knowl-Based Eng Sci 4(1):55–77

    Article  Google Scholar 

  • Eskandari S (2017) A new approach for forest fire risk modeling using fuzzy AHP and GIS in Hyrcanian forests of Iran. Arab J Geosci 10(8):1–13

    Article  Google Scholar 

  • Eskandari S, Chuvieco E (2015) Fire danger assessment in Iran based on geospatial information. Int J Appl Earth Obs Geoinf 42:57–64

    Google Scholar 

  • Eskandari S, Amiri M, Sãdhasivam N, Pourghasemi HR (2020) 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

    Article  Google Scholar 

  • Fernandes K, Munoz A, Bell MA (2020) Combining sub-seasonal time scale precipitation forecast and vegetation health to predict fire anomalies in the Amazon biome. In: AGU Fall Meeting Abstracts, vol 2020, pp GC103–0002. https://doi.org/10.5194/egusphere-egu23-2085

  • Gholizadeh H, Naqinezhad A, Chytrý M (2020) Classification of the Hyrcanian forest vegetation, Northern Iran. Appl Veg Sci 23(1):107–126

    Article  Google Scholar 

  • Gibson R, Danaher T, Hehir W, Collins L (2020) A remote sensing approach to mapping fire severity in south-eastern Australia using sentinel 2 and random forest. Remote Sens Environ 240:111702

    Article  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

    Article  Google Scholar 

  • Gong A, Li J, Chen Y (2021) A spatio-temporal brightness temperature prediction method for forest fire detection with MODIS data: a case study in San Diego. Remote Sens 13(15):2900

    Article  Google Scholar 

  • Gudivada V, Apon A, Ding J (2017) Data quality considerations for big data and machine learning: going beyond data cleaning and transformations. Int J Adv Softw 10(1):1–20

    Google Scholar 

  • Guo F, Wang G, Su Z, Liang H, Wang W, Lin F, Liu A (2016) What drives forest fire in Fujian, China? Evidence from logistic regression and Random Forests. Int J Wildland Fire 25(5):505–519

    Article  Google Scholar 

  • Hamadeh N, Daya B, Hilal A, Chauvet P (2015) An analytical review on the most widely used meteorological models in forest fire prediction. In: 2015 third international conference on technological advances in electrical, electronics and computer engineering (TAEECE). IEEE, pp 239–244. https://doi.org/10.1109/taeece.2015.7113633

  • Jain A, Patel H, Nagalapatti L, Gupta N, Mehta S, Guttula S, ... Munigala V (2020) Overview and importance of data quality for machine learning tasks. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 3561–3562. https://doi.org/10.1145/3394486.3406477

  • 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

    Article  Google Scholar 

  • Kasischke ES, Christensen NL Jr, Stocks BJ (1995) Fire, global warming, and the carbon balance of boreal forests. Ecol Appl 5(2):437–451

    Article  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 Sens Environ 237:111525

    Article  Google Scholar 

  • Keenan RJ, Reams GA, Achard F, de Freitas JV, Grainger A, Lindquist E (2015) Dynamics of global forest area: results from the FAO Global Forest Resources Assessment 2015. For Ecol Manag 352:9–20

    Article  Google Scholar 

  • Kumar SV, Peters-Lidard CD, Tian Y, Houser PR, Geiger J, Olden S et al (2006) Land information system: an interoperable framework for high resolution land surface modeling. Environ Model Softw 21(10):1402–1415

    Article  Google Scholar 

  • Kumari B, Pandey AC (2020) MODIS based forest fire hotspot analysis and its relationship with climatic variables. Spat Inf Res 28(1):87–99

    Article  Google Scholar 

  • Langford Z, Kumar J, Hoffman F (2018) Wildfire mapping in Interior Alaska using deep neural networks on imbalanced datasets. In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, pp 770–778 https://doi.org/10.1109/icdmw.2018.00116

  • Livingston F (2005) Implementation of Breiman’s random forest machine learning algorithm. ECE591Q Machine Learning Journal Paper 1–13. https://doi.org/10.7717/peerj-cs.523/fig-8

  • Ma W, Feng Z, Cheng Z, Chen S, Wang F (2020) Identifying forest fire driving factors and related impacts in China using random forest algorithm. Forests 11(5):507

    Article  Google Scholar 

  • Mackey B (2002) Wildlife, fire & future climate: a forest ecosystem analysis. Csiro Publishing. https://doi.org/10.1071/9780643090040

  • Masocha M, Dube T, Mpofu NT, Chimunhu S (2018) Accuracy assessment of MODIS active fire products in southern African savannah woodlands. Afr J Ecol 56(3):563–571

    Article  Google Scholar 

  • McNally A (2018) FLDAS noah land surface model L4 global monthly 0.1× 0.1 degree (MERRA-2 and CHIRPS). Atmos Compos Water Energy Cycles Clim Var. https://doi.org/10.5067/5NHC22T9375G

  • Meehl GA, Covey C, McAvaney B, Latif M, Stouffer RJ (2005) Overview of the coupled model intercomparison project. Bull Am Meteor Soc 86(1):89–93

    Google Scholar 

  • Milanović S, Marković N, Pamučar D, Gigović L, Kostić P, Milanović SD (2020) Forest fire probability mapping in eastern Serbia: logistic regression versus random forest method. Forests 12(1):5

    Article  Google Scholar 

  • Miranda AI, Coutinho M, Borrego C (1994) Forest fire emissions in Portugal: a contribution to global warming? Environ Pollut 83(1–2):121–123

    Article  CAS  Google Scholar 

  • Mohajane M, Costache R, Karimi F, Pham QB, Essahlaoui A, Nguyen H et al (2021) Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area. Ecol Ind 129:107869

    Article  Google Scholar 

  • Morales-Hidalgo D, Oswalt SN, Somanathan E (2015) Status and trends in global primary forest, protected areas, and areas designated for conservation of biodiversity from the Global Forest Resources Assessment 2015. For Ecol Manag 352:68–77

    Article  Google Scholar 

  • Naganna SR, Beyaztas BH, Bokde N, Armanuos AM (2020) On the evaluation of the gradient tree boosting model for groundwater level forecastinG. Knowl-Based Eng Sci 1(01):48–57

    Article  Google Scholar 

  • Najafabadi ATP, Gorgani F, Najafabadi MO (2015) Modeling forest fires in mazandaran province, Iran. J For Res 26(4):851–858

    Article  Google Scholar 

  • Oris F, Asselin H, Ali AA, Finsinger W, Bergeron Y (2014) Effect of increased fire activity on global warming in the boreal forest. Environ Rev 22(3):206–219

    Article  Google Scholar 

  • Palazzi E, Mortarini L, Terzago S, Von Hardenberg J (2019) Elevation-dependent warming in global climate model simulations at high spatial resolution. Clim Dyn 52(5–6):2685–2702

    Article  Google Scholar 

  • Pastro LA, Dickman CR, Letnic M (2011) Burning for biodiversity or burning biodiversity? Prescribed burn vs. wildfire impacts on plants, lizards, and mammals. Ecol Appl 21(8):3238–3253

    Article  Google Scholar 

  • Pourtaghi ZS, Pourghasemi HR, Rossi M (2015) Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran. Environ Earth Sci 73(4):1515–1533

    Article  Google Scholar 

  • Rosadi D, Andriyani W, Arisanty D, Agustina D (2020) Prediction of forest fire occurrence in peatlands using machine learning approaches. In 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), IEEE, pp 48–51

  • Rosadi D, Andriyani W, Arisanty D, Agustina D (2021) Prediction of forest fire using hybrid SOM-AdaBoost method. J Phys Conf Ser 2123(1):012030. IOP Publishing. https://doi.org/10.1088/1742-6596/2123/1/012030

  • 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

    Article  Google Scholar 

  • Sakr GE, Elhajj IH, Mitri G (2011) Efficient forest fire occurrence prediction for developing countries using two weather parameters. Eng Appl Artif Intell 24(5):888–894

    Article  Google Scholar 

  • Santi E, Clarizia MP, Comite D, Dente L, Guerriero L, Pierdicca N (2022) Detecting fire disturbances in forests by using GNSS reflectometry and machine learning: a case study in Angola. Remote Sens Environ 270:112878

    Article  Google Scholar 

  • Satir O, Berberoglu S, Donmez C (2016) Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem. Geomat Nat Haz Risk 7(5):1645–1658

    Article  Google Scholar 

  • Sevinc V, Kucuk O, Goltas M (2020) A Bayesian network model for prediction and analysis of possible forest fire causes. For Ecol Manag 457:117723

    Article  Google Scholar 

  • Shafiei AB, Akbarinia M, Jalali G, Hosseini M (2010) Forest fire effects in beech dominated mountain forest of Iran. For Ecol Manag 259(11):2191–2196

    Article  Google Scholar 

  • Sharaff A, Gupta H (2019) Extra-tree classifier with metaheuristics approach for email classification. In: Advances in computer communication and computational sciences: proceedings of IC4S 2018. Springer, Singapore, pp 189–197. https://doi.org/10.1007/978-981-13-6861-5_17

  • Silva P, Bastos A, DaCamara CC, Libonati R (2016) Future projections of fire occurrence in Brazil using EC-Earth climate model. Rev Bras De Meteorol 31:288–297

    Article  Google Scholar 

  • Soliman H, Sudan K, Mishra A (2010) A smart forest-fire early detection sensory system: another approach of utilizing wireless sensor and neural networks. In: SENSORS, 2010 IEEE. IEEE, pp 1900–1904. https://doi.org/10.1109/icsens.2010.5690033

  • Strobl C, Malley J, Tutz G (2009) An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychol Methods 14(4):323

    Article  Google Scholar 

  • Sunar F, Özkan C (2001) Forest fire analysis with remote sensing data. Int J Remote Sens 22(12):2265–2277

    Article  Google Scholar 

  • Thom D, Seidl R (2016) Natural disturbance impacts on ecosystem services and biodiversity in temperate and boreal forests. Biol Rev 91(3):760–781

    Article  Google Scholar 

  • Tohidifar M, Moser M, Zehzad B, Ghadirian T (2016) Biodiversity of the hyrcanian forests. A synthesis report. DO, I 10

  • Veblen TT, Kitzberger T, Donnegan J (2000) Climatic and human influences on fire regimes in ponderosa pine forests in the Colorado Front Range. Ecol Appl 10(4):1178–1195

    Article  Google Scholar 

  • Wang Y, Wang J, Du W, Wang C, Liang Y, Zhou C, Huang L (2009) Immune particle swarm optimization for support vector regression on forest fire prediction. In: Advances in neural networks–ISNN 2009: 6th international symposium on neural networks, ISNN 2009 Wuhan, China, May 26–29, 2009 Proceedings, Part II 6. Springer, Berlin, Heidelberg, pp 382–390. https://doi.org/10.1007/978-3-642-01510-6_44

  • Wu X, Gao Y, Jiao D (2019) Multi-label classification based on random forest algorithm for non-intrusive load monitoring system. Processes 7(6):337

    Article  Google Scholar 

  • Wyser K, Kjellström E, Koenigk T, Martins H, Döscher R (2020) Warmer climate projections in EC-Earth3-Veg: the role of changes in the greenhouse gas concentrations from CMIP5 to CMIP6. Environ Res Lett 15(5):54020

    Article  Google Scholar 

  • Yaseen ZM (2023) A new benchmark on machine learning methodologies for hydrological processes modelling: a comprehensive review for limitations and future research directions. Knowl-Based Eng Sci 4(3):65–103

    Article  Google Scholar 

  • Zarghami M, Abdi A, Babaeian I, Hassanzadeh Y, Kanani R (2011) Impacts of climate change on runoffs in East Azerbaijan, Iran. Glob Planet Chang 78(3–4):137–146

    Article  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

    Article  CAS  Google Scholar 

  • Zhao J, Zhang Z, Han S, Qu C, Yuan Z, Zhang D (2011) SVM based forest fire detection using static and dynamic features. Comput Sci Inf Syst 8(3):821–841

    Article  Google Scholar 

  • Zheng Z, Wang J, Shan B, He Y, Liao C, Gao Y, Yang S (2020) A new model for transfer learning-based mapping of burn severity. Remote Sens 12(4):708

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Environmental Resources Engineering, State University of New York College of Environmental Science and Forestry, Syracuse, NY, 13210, USA

    Seyed Babak Haji Seyed Asadollah

  2. Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Ahmad Sharafati

  3. New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Nasiriyah, 64001, Iraq

    Ahmad Sharafati

  4. Department of Mechanical and Construction Engineering, Northumbria University, Newcastle Upon Tyne, NE1 8QH, UK

    Davide Motta

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  1. Seyed Babak Haji Seyed Asadollah
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Seyed Babak Haji Seyed Asadollah proposed the topic and carried out the review analysis, modeling, and drafting the manuscript. Ahmad Sharafati coordinated and assisted in interpreting results and paper editing, carried out visualization, and edited the manuscript. Davide Motta carried out the visualization and paper editing and contributed to the interpretation of the results. All authors read and approved the final manuscript.

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Correspondence to Ahmad Sharafati.

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Asadollah, S.B.H.S., Sharafati, A. & Motta, D. Satellite-based ensemble intelligent approach for predicting forest fire: a case of the Hyrcanian forest in Iran. Environ Sci Pollut Res 31, 22830–22846 (2024). https://doi.org/10.1007/s11356-024-32615-4

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