Advancing forest fire prediction: A multi-layer stacking ensemble model approach (original) (raw)
References
Abdel-Rahman EM, Ahmed FB, Ismail R (2013) Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data. Int J Remote Sens 34(2):712–728 Google Scholar
Abid F (2021) A survey of machine learning algorithms based forest fires prediction and detection systems. Fire Technol 57(2):559–590 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 65:1723–1743 Google Scholar
Ahmed MR, Hassan QK (2023) Occurrence, area burned, and seasonality trends of forest fires in the natural subregions of Alberta over 1959–2021. Fire 6(3):96 Google Scholar
Akram M, Hayat U, Shi J, Anees SA (2022) Association of the Female Flight Ability of Asian Spongy Moths (Lymantria dispar asiatica) with locality, age and mating: a Case Study from China. Forests 13(8):1158. https://doi.org/10.3390/f13081158 Article Google Scholar
Alexander ME (1985) Estimating the length-to-breadth ratio of elliptical forest fire patterns. Proc Eighth Conf Fire For Meteorol 29:84–85 Google Scholar
Alonso-Betanzos A, Fontenla-Romero O, Guijarro-Berdiñas B, Hernández-Pereira E, Andrade MIP, Jiménez E, Soto JLL, Carballas T (2003) An intelligent system for forest fire risk prediction and fire fighting management in Galicia. Expert Syst Appl 25(4):545–554 Google Scholar
Andreevich UV, Reza SSO, Stepanovich TI, Amirhossein A, Meng Z, Anees SA, Petrovich CV (2020) Are there differences in the response of natural stand and plantation biomass to changes in temperature and precipitation? A case for two-needled pines in Eurasia. J Resour Ecol 11(4):331. https://doi.org/10.5814/j.issn.1674-764x.2020.04.001 Article Google Scholar
Anees SA, Zhang X, Khan KA, Abbas M, Ghramh HA, Ahmad Z (2022a) Estimation of fractional vegetation cover dynamics and its drivers based on multi-sensor data in Dera Ismail Khan, Pakistan. J King Saud University-Science 34(6):102217. https://doi.org/10.1016/j.jksus.2022.102217 Article Google Scholar
Anees SA, Zhang X, Shakeel M, Al-Kahtani MA, Khan KA, Akram M, Ghramh HA (2022b) Estimation of fractional vegetation cover dynamics based on satellite remote sensing in Pakistan: a comprehensive study on the FVC and its drivers. J King Saud University-Science 34(3):101848. https://doi.org/10.1016/j.jksus.2022.101848 Article Google Scholar
Anees SA, Mehmood K, Khan WR, Sajjad M, Alahmadi TA, Alharbi SA, Luo M (2024a) Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the himalayan region. Ecol Inf 102732. https://doi.org/10.1016/j.ecoinf.2024.102732
Anees SA, Mehmood K, Raza SIH, Pfautsch S, Shah M, Jamjareegulgarn P, Shahzad F, Alarfaj AA, Alharbi SA, Khan WR, Dube T (2024b) Spatiotemporal analysis of surface Urban Heat Island intensity and the role of vegetation in six major Pakistani cities. Ecological Informatics. 102986. https://doi.org/10.1016/j.ecoinf.2024.102986
Anees SA, Mehmood K, Rehman A, Rehman NU, Muhammad S, Shahzad F, Hussain K, Luo M, Alarfaj AA, Alharbi SA, Khan WR (2024c) Unveiling fractional vegetation cover dynamics: a spatiotemporal analysis using MODIS NDVI and Machine Learning. Environ Sustain Indic 100485. https://doi.org/10.1016/j.indic.2024.100485
Anees SA, Yang X, Mehmood K (2024d) The stoichiometric characteristics and the relationship with hydraulic and morphological traits of the Faxon fir in the subalpine coniferous forest of Southwest China. Ecol Ind 159:111636. https://doi.org/10.1016/j.ecolind.2024.111636 Article Google Scholar
Anees SA, Mehmood K, Khan WR, Shahzad F, Zhran M, Ayub R, Alarfaj AA, Alharbi SA, Liu Q (2025) Spatiotemporal dynamics of vegetation cover: integrative machine learning analysis of multispectral imagery and environmental predictors. Earth Sci Inf 18(1):1–23. https://doi.org/10.1007/s12145-024-01673-0 Article Google Scholar
Aslam MS, Huanxue P, Sohail S, Majeed MT, Rahman SU, Anees SA (2022) Assessment of major food crops production-based environmental efficiency in China, India, and Pakistan. Environmental Science and Pollution Research, pp 1–10. https://doi.org/10.1007/s11356-021-16161-x Book Google Scholar
Atchley AL, Linn R, Jonko A, Hoffman C, Hyman JD, Pimont F, Sieg C, Middleton RS (2021) Effects of fuel spatial distribution on wildland fire behaviour. Int J Wildland Fire 30(3):179–189 Google Scholar
Awad M, Fraihat S (2023) Recursive feature elimination with cross-validation with decision tree: feature selection method for machine learning-based intrusion detection systems. J Sens Actuator Networks 12(5). https://doi.org/10.3390/jsan12050067
Badshah MT, Hussain K, Rehman AU, Mehmood K, Muhammad B, Wiarta R, Meng J (2024) The role of random forest and Markov chain models in understanding metropolitan urban growth trajectory. Front Forests Global Change 7:1345047 Google Scholar
Baptista ML, Goebel K, Henriques EMP (2022) Relation between prognostics predictor evaluation metrics and local interpretability SHAP values. Artificial Intelligence, 306. https://doi.org/10.1016/j.artint.2022.103667
Barreto JS, Armenteras D (2020) Open data and machine learning to model the occurrence of fire in the ecoregion of llanos colombo–venezolanos. Remote Sens 12(23):3921 Google Scholar
Begum BA, Biswas SK, Pandit GG, Saradhi IV, Waheed S, Siddique N, Seneviratne MCS, Cohen DD, Markwitz A, Hopke PK (2011) Long–range transport of soil dust and smoke pollution in the South Asian Region. Atmospheric Pollution Res 2(2):151–157 CAS Google Scholar
Benali A, Sá ACL, Ervilha AR, Trigo RM, Fernandes PM, Pereira JMC (2017) Fire spread predictions: sweeping uncertainty under the rug. Sci Total Environ 592:187–196 CAS Google Scholar
Bhadoria RS, Pandey MK, Kundu P (2021) RVFR: random vector forest regression model for integrated & enhanced approach in forest fires predictions. Ecol Inf 66:101471 Google Scholar
Bjånes A, De La Fuente R, Mena P (2021) A deep learning ensemble model for wildfire susceptibility mapping. Ecol Inf 65:101397 Google Scholar
Botequim B, Arias-Rodil M, Garcia-Gonzalo J, Silva A, Marques S, Borges JG, Oliveira MM, Tomé M (2017) Modeling post-fire mortality in pure and mixed forest stands in Portugal—A forest planning-oriented model. Sustainability 9(3):390 Google Scholar
Boubeta M, Lombardía MJ, Marey-Pérez MF, Morales D (2015) Prediction of forest fires occurrences with area-level Poisson mixed models. J Environ Manage 154:151–158 Google Scholar
Brown AR, Petropoulos GP, Ferentinos KP (2018) Appraisal of the Sentinel-1 & 2 use in a large-scale wildfire assessment: a case study from Portugal’s fires of 2017. Appl Geogr 100:78–89 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. Ecol Inf 48:104–116 Google Scholar
Cai G, Zheng X, Gao W, Guo J (2024) Self-extinction characteristics of fire extinguishing induced by nitrogen injection rescue in an enclosed urban utility tunnel. Case Stud Therm Eng 59:104478. https://doi.org/10.1016/j.csite.2024.104478 Article Google Scholar
Cakiroglu C, Demir S, Ozdemir MH, Aylak BL, Sariisik G, Abualigah L (2024) Data-driven interpretable ensemble learning methods for the prediction of wind turbine power incorporating SHAP analysis. Expert Syst Appl 237:121464 Google Scholar
Chang Y, Zhu Z, Bu R, Chen H, Feng Y, Li Y, Hu Y, Wang Z (2013) Predicting fire occurrence patterns with logistic regression in Heilongjiang Province, China. Landscape Ecol 28:1989–2004 Google Scholar
Chang Y-S, Abimannan S, Chiao H-T, Lin C-Y, Huang Y-P (2020) An ensemble learning based hybrid model and framework for air pollution forecasting. Environ Sci Pollut Res 27:38155–38168 Google Scholar
Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.293978
Chen F, Du Y, Niu S, Zhao J (2015) Modeling forest lightning fire occurrence in the Daxinganling Mountains of Northeastern China with MAXENT. Forests 6(5):1422–1438 Google Scholar
Chen Y, Zheng W, Li W, Huang Y (2021) Large group activity security risk assessment and risk early warning based on random forest algorithm. Pattern Recognit Lett 144:1–5 Google Scholar
Chuvieco E, Giglio L, Justice C (2008) Global characterization of fire activity: toward defining fire regimes from Earth observation data. Glob Change Biol 14(7):1488–1502 Google Scholar
Contreras P, Orellana-Alvear J, Muñoz P, Bendix J, Célleri R (2021) Influence of random forest hyperparameterization on short-term runoff forecasting in an andean mountain catchment. Atmosphere 12(2). https://doi.org/10.3390/atmos12020238
Denham M, Cortés A, Margalef T, Luque E (2008) Applying a dynamic data driven genetic algorithm to improve forest fire spread prediction. Int Conf Comput Sci 36–45. https://doi.org/10.1007/978-3-540-69389-5_6
Derbentsev V, Babenko V, Khrustalev K, Obruch H, Khrustalova S (2021) Comparative performance of machine learning ensemble algorithms for forecasting cryptocurrency prices. Int J Eng 34(1):140–148 Google Scholar
Didan K (2021) MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center. Accessed 2024-04-20 from https://doi.org/10.5067/MODIS/MOD13Q1.061
DiMiceli C, Sohlberg R, Townshend J (2022) MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250m SIN Grid V061 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center. Accessed 2024-04-20 from https://doi.org/10.5067/MODIS/MOD44B.061
Dong SQ, Sun YM, Xu T, Zeng LB, Du XY, Yang X, Liang Y (2023) How to improve machine learning models for lithofacies identification by practical and novel ensemble strategy and principles. Pet Sci 20(2). https://doi.org/10.1016/j.petsci.2022.09.006
Duff TJ, Tolhurst KG (2015) Operational wildfire suppression modelling: a review evaluating development, state of the art and future directions. Int J Wildland Fire 24(6):735–748 Google Scholar
Duro DC, Franklin SE, Dubé MG (2012) Multi-scale object-based image analysis and feature selection of multi-sensor earth observation imagery using random forests. Int J Remote Sens 33(14):4502–4526 Google Scholar
Dutta R, Das A, Aryal J (2016) Big data integration shows Australian bush-fire frequency is increasing significantly. Royal Soc Open Sci 3(2):150241 Google Scholar
Eugenio FC, dos Santos AR, Fiedler NC, Ribeiro GA, da Silva AG, dos Santos ÁB, Paneto GG, Schettino VR (2016) Applying GIS to develop a model for forest fire risk: a case study in Espírito Santo, Brazil. J Environ Manage 173:65–71 Google Scholar
Ganteaume A, Camia A, Jappiot M, San-Miguel-Ayanz J, Long-Fournel M, Lampin C (2013) A review of the main driving factors of forest fire ignition over Europe. Environ Manage 51:651–662 Google Scholar
Gao E, Zhou G, Li S, Fu B, Xiao Y, Lan Y, Bai Y (2024) Spatio-temporal evolution monitoring and analysis of Tidal Flats in Beibu Gulf from 1987 to 2021 using Multisource Remote sensing. IEEE J Sel Top Appl Earth Observations Remote Sens 17:6099–6114. https://doi.org/10.1109/JSTARS.2024.3398604 Article Google Scholar
Garcia CV, Woodard PM, Titus SJ, Adamowicz WL, Lee BS (1995) A logit model for predicting the daily occurrence of human caused forest-fires. Int J Wildland Fire 5(2):101–111 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 Google Scholar
Gitas I, Mitri G, Veraverbeke S, Polychronaki A (2012) Advances in remote sensing of post-fire vegetation recovery monitoring—A review. Remote Sens Biomass-Principles Appl 1:334 Google Scholar
Guan R (2023) Predicting forest fire with linear regression and random forest. Highlights Sci Eng Technol 44:1–7 Google Scholar
Guo F, Zhang L, Jin S, Tigabu M, Su Z, Wang W (2016) Modeling anthropogenic fire occurrence in the boreal forest of China using logistic regression and random forests. Forests 7(11):250 CAS Google Scholar
Haider K, Khokhar MF, Chishtie F, RazzaqKhan W, Hakeem KR (2017) Identification and future description of warming signatures over Pakistan with special emphasis on evolution of CO 2 levels and temperature during the first decade of the twenty-first century, vol 24. Environmental Science and Pollution Research, pp 7617–7629 Google Scholar
Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau D, Stehman SV, Goetz SJ, Loveland TR (2013) High-resolution global maps of 21st-century forest cover change. Science 342(6160):850–853 CAS Google Scholar
Healey SP, Cohen WB, Yang Z, Brewer CK, Brooks EB, Gorelick N, Hernandez AJ, Huang C, Hughes MJ, Kennedy RE (2018) Mapping forest change using stacked generalization: an ensemble approach. Remote Sens Environ 204:717–728 Google Scholar
Hong H, Tsangaratos P, Ilia I, Liu J, Zhu A-X, Xu C (2018) Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models. The case of Dayu County, China. Sci Total Environ 630:1044–1056 CAS Google Scholar
Hu T, Yang J, Li X, Gong P (2016) Mapping urban land use by using landsat images and open social data. Remote Sens 8(2):151 CAS Google Scholar
Hussain K, Mehmood K, Yujun S, Badshah T, Anees SA, Shahzad F, Nooruddin, Ali J, Bilal M (2024) Analysing LULC transformations using remote sensing data: insights from a multilayer perceptron neural network approach. Ann GIS 1–27. https://doi.org/10.1080/19475683.2024.2343399
Iban MC, Aksu O (2024) SHAP-Driven explainable Artificial Intelligence Framework for Wildfire susceptibility mapping using MODIS active fire pixels: an In-Depth interpretation of contributing factors in Izmir, Türkiye. Remote Sens 16(15):2842 Google Scholar
Ireland G, Petropoulos GP (2015) Exploring the relationships between post-fire vegetation regeneration dynamics, topography and burn severity: a case study from the Montane Cordillera Ecozones of Western Canada. Appl Geogr 56:232–248 Google Scholar
Jain P, Wang X, Flannigan MD (2017) Trend analysis of fire season length and extreme fire weather in North America between 1979 and 2015. Int J Wildland Fire 26(12):1009–1020 Google Scholar
Jain P, Coogan SCP, Subramanian SG, Crowley M, Taylor S, Flannigan MD (2020) A review of machine learning applications in wildfire science and management. Environ Reviews 28(4):478–505 Google Scholar
Jallat H, Khokhar MF, Kudus KA, Nazre M, Saqib NU, Tahir U, Khan WR (2021) Monitoring carbon stock and land-use change in 5000-year-old juniper forest stand of Ziarat, Balochistan, through a synergistic approach. Forests 12(1):51 Google Scholar
Jing X, Zhang D, Li X, Zhang W, Zhang Z (2023) Prediction of forest fire occurrence in southwestern China. Forests 14(9):1797 Google Scholar
Johnson NE, Ianiuk O, Cazap D, Liu L, Starobin D, Dobler G, Ghandehari M (2017) Patterns of waste generation: a gradient boosting model for short-term waste prediction in New York City. Waste Manag 62:3–11 Google Scholar
Jurečka F, Možný M, Balek J, Žalud Z, Trnka M (2019) Comparison of methods for the assessment of fire danger in the Czech Republic. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 67(5). https://doi.org/10.11118/actaun201967051285
Kaur P, Singh A, Chana I (2022) BSense: a parallel bayesian hyperparameter optimized stacked ensemble model for breast cancer survival prediction. J Comput Sci 60. https://doi.org/10.1016/j.jocs.2022.101570
Khan WR, Rasheed F, Zulkifli SZ, Kasim MRBM, Zimmer M, Pazi AM, Kamrudin NA, Zafar Z, Faridah-Hanum I, Nazre M (2020) Phytoextraction potential of Rhizophora apiculata: a case study in Matang mangrove forest reserve, Malaysia, vol 13. Tropical Conservation Science, p 1940082920947344 Google Scholar
Khan WR, Nazre M, Akram S, Anees SA, Mehmood K, Ibrahim FH, Zhu X (2024) Assessing the Productivity of the Matang Mangrove Forest Reserve: review of one of the best-managed Mangrove forests. Forests 15(5):747. https://doi.org/10.3390/f15050747 Article Google Scholar
Kim K, Jeong J (2022) Multi-layer stacking ensemble for fault detection classification in hydraulic system. 2022 26th International Conference on Circuits, Systems, Communications and Computers (CSCC), 341–346. https://doi.org/10.1109/CSCC55931.2022.00066
Kim HS, Kim HS, Choi SY (2024) Investigating the Impact of Agricultural, Financial, Economic, and political factors on Oil Forward prices and volatility: a SHAP analysis. Energies 17(5). https://doi.org/10.3390/en17051001
Köhl M, Lasco R, Cifuentes M, Jonsson Ö, Korhonen KT, Mundhenk P, de Jesus Navar J, Stinson G (2015) Changes in forest production, biomass and carbon: results from the 2015 UN FAO Global Forest Resource Assessment. For Ecol Manag 352:21–34 Google Scholar
Lefever DW (1926) Measuring geographic concentration by means of the standard deviational ellipse. Am J Sociol 32(1):88–94 Google Scholar
Li Y, Feng Z, Chen S, Zhao Z, Wang F (2020) Application of the artificial neural network and support vector machines in forest fire prediction in the guangxi autonomous region, China. Discrete Dynamics Nat Soc 2020:1–14 Google Scholar
Li Y, Li G, Wang K, Wang Z, Chen Y (2023) Forest Fire Risk Prediction based on stacking ensemble learning for Yunnan Province of China. Fire 7(1):13 Google Scholar
Liang HL, Lin YR, Yang G, Su Z, Wang W, Guo F (2016) Application of random forest algorithm on the forest fire prediction in Tahe area based on meteorological factors. Sci Silvae Sin 52:89–98 Google Scholar
Lieske DJ, Schmid MS, Mahoney M (2018) Ensembles of ensembles: combining the predictions from multiple machine learning methods. Mach Learn Ecol Sustainable Nat Resource Manage. 109–121. https://doi.org/10.1007/978-3-319-96978-7_5
Linfei CAI, Dasheng WU, Luming F, Xinyu Z (2019) Tree species identification using XGBoost based on GF-2 images. For Resour WANAGEMENT 5:44 Google Scholar
Luo M, Anees SA, Huang Q, Qin X, Qin Z, Fan J, Han G, Zhang L, Shafri HZM (2024) Improving forest above-ground biomass estimation by integrating individual machine learning models. Forests 15(6):975. https://doi.org/10.3390/f15060975 Article Google Scholar
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 Google Scholar
Mannan A, Feng Z, Ahmad A, Beckline M, Saeed S, Liu J, Shah S, Amir M, Ammara U, Ullah T (2017) CO2 emission trends and risk zone mapping of forest fires in subtropical and moist temperate forests of Pakistan. Appl Ecol Environ Res 17(2):2983–3002 Google Scholar
Marques S, Garcia-Gonzalo J, Botequim B, Ricardo A, Borges JG, Tomé M, Oliveira MM (2012) Assessing wildfire occurrence probability in Pinus pinaster Ait. Stands in Portugal. For Syst 21:111–120 Google Scholar
Marston CG, Danson FM, Armitage RP, Giraudoux P, Pleydell DRJ, Wang Q, Qui J, Craig PS (2014) A random forest approach for predicting the presence of Echinococcus Multilocularis intermediate host Ochotona spp. presence in relation to landscape characteristics in western China. Appl Geogr 55:176–183 Google Scholar
Martell DL, Otukol S, Stocks BJ (1987) A logistic model for predicting daily people-caused forest fire occurrence in Ontario. Can J for Res 17(5):394–401 Google Scholar
Martínez J, Vega-Garcia C, Chuvieco E (2009) Human-caused wildfire risk rating for prevention planning in Spain. J Environ Manage 90(2):1241–1252 Google Scholar
Meddour-Sahar O (2015) Wildfires in Algeria: problems and challenges. IForest-Biogeosciences Forestry 8(6):818 Google Scholar
Mehmood K, Anees SA, Luo M, Akram M, Zubair M, Khan KA, Khan WR (2024a) Assessing Chilgoza Pine (Pinus gerardiana) Forest Fire Severity: remote sensing analysis, correlations, and Predictive modeling for enhanced management strategies. Trees, Forests and People, p 100521. https://doi.org/10.1016/j.tfp.2024.100521 Book Google Scholar
Mehmood K, Anees SA, Muhammad S, Hussain K, Shahzad F, Liu Q, Ansari MJ, Alharbi SA, Khan WR (2024b) Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables. Sci Rep 14(1):11775. https://doi.org/10.1038/s41598-024-62464-7 ArticleCAS Google Scholar
Mehmood K, Anees SA, Rehman A, Rehman NU, Muhammad S, Shahzad F, Liu Q, Alharbi SA, Alfarraj S, Ansari MJ, Khan WR (2024c) Assessment of climatic influences on net primary productivity along elevation gradients in temperate ecoregions. Trees, Forests and People, p 100657. https://doi.org/10.1016/j.tfp.2024.100657
Mehmood K, Anees SA, Rehman A, Tariq A, Liu Q, Muhammad S, Rabbi F, Pan SA, Hatamleh WA (2024d) Assessing forest cover changes and fragmentation in the Himalayan Temperate Region: implications for forest conservation and management. J Forestry Res 35(1):82. https://doi.org/10.1007/s11676-024-01734-6 Article Google Scholar
Mehmood K, Anees SA, Rehman A, Tariq A, Zubair M, Liu Q, Rabbi F, Khan KA, Luo M (2024e) Exploring spatiotemporal dynamics of NDVI and climate-driven responses in ecosystems: insights for sustainable management and climate resilience. Ecol Inf 102532. https://doi.org/10.1016/j.ecoinf.2024.102532
Meng S, Zhang C, Shi Q, Chen Z, Hu W, Lu F (2023) A robust Infrared Small Target Detection Method Jointing multiple information and noise prediction: Algorithm and Benchmark. IEEE Trans Geosci Remote Sens 61:1–17. https://doi.org/10.1109/TGRS.2023.3295932 Article Google Scholar
Milanović S, Kaczmarowski J, Ciesielski M, Trailović Z, Mielcarek M, Szczygieł R, Kwiatkowski M, Bałazy R, Zasada M, Milanović SD (2022) Modeling and mapping of forest fire occurrence in the Lower Silesian Voivodeship of Poland based on machine learning methods. Forests 14(1):46 Google Scholar
Miller JD, Thode AE (2007) Quantifying burn severity in a heterogeneous landscape with a relative version of the delta normalized burn ratio (dNBR). Remote Sens Environ 109(1):66–80 Google Scholar
Mittal M, Siriaraya P, Lee C, Kawai Y, Yoshikawa T, Shimojo S (2019) Accurate spatial mapping of social media data with physical locations. _2_019 IEEE International Conference on Big Data (Big Data) 4113–4116. https://doi.org/10.1109/BigData47090.2019.9006477
Moncada-Torres A, van Maaren MC, Hendriks MP, Siesling S, Geleijnse G (2021) Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival. Sci Rep 11(1). https://doi.org/10.1038/s41598-021-86327-7
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 Google Scholar
Morgan P, Hardy CC, Swetnam TW, Rollins MG, Long DG (2001) Mapping fire regimes across time and space: understanding coarse and fine-scale fire patterns. Int J Wildland Fire 10(4):329–342 Google Scholar
Muhammad S, Hamza A, Mehmood K, Adnan M, Tayyab M (2023) Analyzing the impact of forest harvesting ban in northern temperate forest. A case study of Anakar Valley, Kalam Swat Region, Khyber-Pakhtunkhwa, Pakistan. Pure Appl Biology 12(2):1434–1439 Google Scholar
Mupfiga UN, Mutanga O, Dube T, Kowe P (2022) Spatial Clustering of Vegetation Fire Intensity Using MODIS Satellite Data. Atmosphere 13(12):1972 Google Scholar
Myneni R, Knyazikhin Y, Park T (2021) MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V061 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center. Accessed 2024-04-20 from https://doi.org/10.5067/MODIS/MOD15A2H.061
Nahiduzzaman M, Abdulrazak F, Arselene Ayari L, Khandakar MA, Islam SMR (2024) A novel framework for lung cancer classification using lightweight convolutional neural networks and ridge extreme learning machine model with SHapley Additive exPlanations (SHAP). Expert Systems with Applications 248. https://doi.org/10.1016/j.eswa.2024.123392
Nhongo EJS, Fontana DC, Guasselli LA, Bremm C (2019) Probabilistic modelling of wildfire occurrence based on logistic regression, Niassa Reserve, Mozambique. Geomatics Nat Hazards Risk 10(1):1772–1792 Google Scholar
Nohara Y, Matsumoto K, Soejima H, Nakashima N (2022) Explanation of machine learning models using shapley additive explanation and application for real data in hospital. Comput Methods Programs Biomed 214. https://doi.org/10.1016/j.cmpb.2021.106584
Oliveira SLJ, Pereira JMC, Carreiras JMB (2011) Fire frequency analysis in Portugal (1975–2005), using landsat-based burnt area maps. Int J Wildland Fire 21(1):48–60 Google Scholar
Oliveira S, Oehler F, San-Miguel-Ayanz J, Camia A, Pereira JMC (2012) Modeling spatial patterns of fire occurrence in Mediterranean Europe using multiple regression and Random Forest. For Ecol Manag 275:117–129 Google Scholar
Özbayoğlu AM, Bozer R (2012) Estimation of the burned area in forest fires using computational intelligence techniques. Procedia Comput Sci 12:282–287 Google Scholar
Pan SA, Anees SA, Li X, Yang X, Duan X, Li Z (2023) Spatial and temporal patterns of non-structural carbohydrates in Faxon Fir (Abies Fargesii var. Faxoniana), Subalpine Mountains of Southwest China. Forests 14(7):1438. https://doi.org/10.3390/f14071438 Article Google Scholar
Pang Y, Li Y, Feng Z, Feng Z, Zhao Z, Chen S, Zhang H (2022) Forest fire occurrence prediction in China based on machine learning methods. Remote Sens 14(21):5546 Google Scholar
Piao ShiLong PS, Huang MengTian HM, Zhuo L, Wang LZ, XuHui WX, Ciais P, Canadell JG, Kai W, Bastos WK, Friedlingstein AP, Houghton RA (2019) Lower land-use emissions responsible for increased net land carbon sink during the slow warming period. Nat Geosci 11:739–743 Google Scholar
Ponce-Bobadilla AV, Schmitt V, Maier CS, Mensing S, Stodtmann S (2024) Practical guide to SHAP analysis: explaining supervised machine learning model predictions in drug development. Clin Transl Sci 17(11):e70056 Google Scholar
Rafaqat W, Iqbal M, Kanwal R, Song W (2022a) Study of driving factors using machine learning to determine the effect of topography, climate, and fuel on wildfire in Pakistan. Remote Sens 14(8):1918 Google Scholar
Rafaqat W, Iqbal M, Kanwal R, Weiguo S (2022b) Evaluation of Wildfire occurrences in Pakistan with Global Gridded Soil Properties Derived from remotely sensed data. Remote Sens 14(21):5503 Google Scholar
Ramezan CA, Warner TA, Maxwell AE (2019) Evaluation of sampling and cross-validation tuning strategies for regional-scale machine learning classification. Remote Sens 11(2). https://doi.org/10.3390/rs11020185
Rodrigues M, San Miguel J, Oliveira S, Moreira F, Camia A (2013) An insight into spatial-temporal trends of fire ignitions and burned areas in the European Mediterranean countries. J Earth Sci Eng 3(7):497 Google Scholar
Rowell A, Moore PF (2000) Global review of forest fires. Forests for Life Programme Unit, WWF International, Gland, Switzerland, pp 66–66
Saeed S, Ashraf MI, Ahmad A, Rahman Z (2016) The Bela forest ecosystem of district Jhelum, a potential carbon sink. Pak J Bot 48(1):121–129 CAS 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 Google Scholar
Sasikala D, Theetchenya S (2024) A comparative exploration of time series models for wild fire prediction. In: 2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT). IEEE pp 1–5
Shahzad F, Mehmood K, Hussain K, Haidar I, Anees SA, Muhammad S, Ali J, Adnan M, Wang Z, Feng Z (2024) Comparing machine learning algorithms to predict vegetation fire detections in Pakistan. Fire Ecol 20(1):1–20. https://doi.org/10.1186/s42408-024-00289-5 Article 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):856 Google Scholar
Shao Y, Feng Z, Cao M, Wang W, Sun L, Yang X, Ma T, Guo Z, Fahad S, Liu X (2023) An ensemble model for forest fire occurrence mapping in China. Forests 14(4):704 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. Int J Wildland Fire 31(8). https://doi.org/10.1071/WF22016
Shaw R (2015) Floods in the Hindu Kush Region: causes and socio-economic aspects. Mountain hazards and disaster risk reduction 33–52
Shobairi SOR, Lin H, Usoltsev VA, Osmirko AA, Tsepordey IS, Ye Z, Anees SA (2022) A comparative pattern for Populus spp. and Betula Spp. Stand Biomass in Eurasian Climate gradients. Croat J For Eng: J Theory Appl For Eng 43(2):457–467. https://doi.org/10.5552/crojfe.2022.1340
Singh KR, Neethu KP, Madhurekaa K, Harita A, Mohan P (2021) Parallel SVM model for forest fire prediction. Soft Comput Lett 3:100014 Google Scholar
Song W, Wang J, Satoh K, Fan W (2005) Impact of population density on forest fire frequency. Fire Saf Sci 14:1–5 Google Scholar
Sukmana HT, Durachman Y, Amri A, Supardi S (2024) Comparative analysis of SVM and RF algorithms for Tsunami Prediction: a performance evaluation study. J Appl Data Sci 5(1):84–99 Google Scholar
Sun L, Feng Z, Shao Y, Wang L, Su J, Ma T, Lu D, An J, Pang Y, Fahad S (2023) The development of a set of Novel Low cost and Data Processing-Free Measuring Instruments for Tree diameter at breast height and tree position. Forests 14(5):891 Google Scholar
Syifa M, Panahi M, Lee C-W (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 Sens 12(4):623 Google Scholar
Tariq A, Shu H, Siddiqui S, Mousa BG, Munir I, Nasri A, Waqas H, Lu L, Baqa MF (2021) Forest fire monitoring using spatial-statistical and Geo-spatial analysis of factors determining forest fire in Margalla Hills, Islamabad, Pakistan. Geomatics Nat Hazards Risk 12(1):1212–1233 Google Scholar
Tariq A, Shu H, Siddiqui S, Munir I, Sharifi A, Li Q, Lu L (2022) Spatio-temporal analysis of forest fire events in the Margalla Hills, Islamabad, Pakistan using socio-economic and environmental variable data with machine learning methods. J For Res 33(1):183–194
Thach NN, Ngo DB-T, Xuan-Canh P, Hong-Thi N, Thi BH, Nhat-Duc H, Dieu TB (2018) Spatial pattern assessment of tropical forest fire danger at Thuan Chau area (Vietnam) using GIS-based advanced machine learning algorithms: a comparative study. Ecol Inf 46:74–85 Google Scholar
Tien Bui D, Le K-TT, Nguyen VC, Le HD, Revhaug I (2016) Tropical forest fire susceptibility mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, using GIS-based kernel logistic regression. Remote Sens 8(4):347 Google Scholar
Topaloğlu RH, Sertel E, Musaoğlu N (2016) Assessment of classification accuracies of Sentinel-2 and Landsat-8 data for land cover/use mapping. Int Archives Photogrammetry Remote Sens Spat Inform Sci 41:1055–1059 Google Scholar
Toujani A, Achour H, Faïz S (2018) Estimating forest fire losses using stochastic approach: case study of the Kroumiria Mountains (Northwestern Tunisia). Appl Artif Intell 32(9–10):882–906 Google Scholar
Usoltsev VA, Chen B, Shobairi SOR, Tsepordey IS, Chasovskikh VP, Anees SA (2020) Patterns for Populus spp. stand biomass in gradients of winter temperature and precipitation of Eurasia. Forests 11(9):906. https://doi.org/10.3390/f11090906 Article Google Scholar
Usoltsev VA, Lin H, Shobairi SOR, Tsepordey IS, Ye Z, Anees SA (2022) The principle of space-for-time substitution in predicting Betula Spp. Biomass change related to climate shifts. Appl Ecol Environ Res 20(4):3683–3698. https://doi.org/10.15666/aeer/2004_36833698 Article Google Scholar
Vadrevu KP, Csiszar I, Ellicott E, Giglio L, Badarinath KVS, Vermote E, Justice C (2012) Hotspot analysis of vegetation fires and intensity in the Indian region. IEEE J Sel Top Appl Earth Observations Remote Sens 6(1):224–238 Google Scholar
Varner JM, Kane JM, Kreye JK, Engber E (2015) The flammability of forest and woodland litter: a synthesis. Curr Forestry Rep 1:91–99 Google Scholar
Vasilakos C, Kalabokidis K, Hatzopoulos J, Kallos G, Matsinos Y (2007) Integrating new methods and tools in fire danger rating. Int J Wildland Fire 16(3):306–316 Google Scholar
Vasilakos C, Kalabokidis K, Hatzopoulos J, Matsinos I (2009) Identifying wildland fire ignition factors through sensitivity analysis of a neural network. Nat Hazards 50:125–143 Google Scholar
Vega-Garcia C, Lee BS, Woodard PM, Titus SJ (1996) Applying neural network technology to human-caused wildfire occurrence prediction
Vega-García C, Chuvieco E (2006) Applying local measures of spatial heterogeneity to Landsat-TM images for predicting wildfire occurrence in Mediterranean landscapes. Landscape Ecol 21:595–605 Google Scholar
Wang S-C, Wang Y (2019) Predicting wildfire burned area in South Central US using integrated machine learning techniques. Atmos Chem Phys Discuss 20:1–25 Google Scholar
Wang N, Xu Y, Wang S (2022) Interpretable boosting tree ensemble method for multisource building fire loss prediction. Reliab Eng Syst Saf 225. https://doi.org/10.1016/j.ress.2022.108587
Wikle CK, Datta A, Hari BV, Boone EL, Sahoo I, Kavila I, Castruccio S, Simmons SJ, Burr WS, Chang W (2023) An illustration of model agnostic explainability methods applied to environmental data. Environmetrics 34(1). https://doi.org/10.1002/env.2772
Wolpert DH (1992) Stacked generalization. Neural Netw 5(2):241–259 Google Scholar
Wu Z, Middleton B, Hetzler R, Vogel J, Dye D (2015) Vegetation burn severity mapping using Landsat-8 and WorldView-2. Photogrammetric Eng Remote Sens 81(2):143–154 Google Scholar
Xie Y, Peng M (2019) Forest fire forecasting using ensemble learning approaches. Neural Comput Appl 31(9):4541–4550 Google Scholar
Xie L, Zhang R, Zhan J, Li S, Shama A, Zhan R, Wang T, Lv J, Bao X, Wu R (2022) Wildfire risk assessment in Liangshan Prefecture, China based on an integration machine learning algorithm. Remote Sens 14(18):4592 Google Scholar
Ye T, Wang Y, Guo Z, Li Y (2017) Factor contribution to fire occurrence, size, and burn probability in a subtropical coniferous forest in East China. PLoS ONE 12(2):e0172110 Google Scholar
Yu Q, Hou Z, Wang Z (2024) Predictive modeling of preoperative acute heart failure in older adults with hypertension: a dual perspective of SHAP values and interaction analysis. BMC Med Inf Decis Mak 24(1):329 Google Scholar
Zaker Esteghamati M, Gernay T, Banerji S (2023) Evaluating fire resistance of timber columns using explainable machine learning models. Eng Struct 296. https://doi.org/10.1016/j.engstruct.2023.116910
Zanaga D, Van De Kerchove R, De Keersmaecker W, Souverijns N, Brockmann C, Quast R, Wevers J, Grosu A, Paccini A, Vergnaud S, Cartus O, Santoro M, Fritz S, Georgieva I, Lesiv M, Carter S, Herold M, Li L, Tsendbazar NE, Ramoino F, Arino O (2021) ESA WorldCover 10 m 2020 v100. https://doi.org/10.5281/zenodo.5571936
Zhai C, Zhang S, Cao Z, Wang X (2020) Learning-based prediction of wildfire spread with real-time rate of spread measurement. Combust Flame 215:333–341 CAS Google Scholar
Zhang J, Chen J, Li X, Chen H, Xie P, Li W (2020) Combining postprocessed ensemble weather forecasts and multiple hydrological models for ensemble streamflow predictions. J Hydrol Eng 25(1):04019060 Google Scholar
Zhang Z, Wang L, Xue N, Du Z (2021) Spatiotemporal analysis of active fires in the Arctic region during 2001–2019 and a fire risk assessment model. Fire 4(3):57 Google Scholar
Zhao P, Zhang F, Lin H, Xu S (2021) GIS-Based Forest Fire Risk Model: a Case Study in Laoshan National Forest Park, Nanjing. Remote Sens 13(18):3704 Google Scholar
Zhou G, Li J, Tian Z, Xu J, Bai Y (2024a) The Extended Stumpf model for water depth Retrieval from Satellite Multispectral images. IEEE J Sel Top Appl Earth Observations Remote Sens 17:6779–6790. https://doi.org/10.1109/JSTARS.2024.3368761 Article Google Scholar