A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data
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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
Althuwaynee OF, Pradhan B, Park HJ, Lee JH (2014) A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping. Landslides 11:1063–1078
Arnett JT, Coops NC, Daniels LD, Falls RW (2015) Detecting forest damage after a low-severity fire using remote sensing at multiple scales. Int J Appl Earth Obs Geoinf 35:239–246
Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides 1:73–81. https://doi.org/10.1007/s10346-003-0006-9
Bassett M et al (2015) The effects of topographic variation and the fire regime on coarse woody debris: insights from a large wildfire. For Ecol Manag 340:126–134
Bednarik M, Magulová B, Matys M, Marschalko M (2010) Landslide susceptibility assessment of the Kraľovany–Liptovský Mikuláš railway case study. Physics Chemistry Earth, Parts A/B/C 35:162–171. https://doi.org/10.1016/j.pce.2009.12.002
Belgiu M, Drăguţ L (2016) Random forest in remote sensing: a review of applications and future directions. ISPRS J Photogramm Remote Sens 114:24–31
Bian S, Wang W Investigation on diversity in homogeneous and heterogeneous ensembles. In: Neural Netw, 2006. IJCNN’06. International Joint Conference on, 2006. IEEE, pp 3078–3085
Breiman L (1996) Bagging predictors. Mach Learn 24:123–140
Bui DT, Tuan TA, Hoang ND, Thanh NQ, Nguyen DB, Van Liem N, Pradhan B (2017) Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization. Landslides 14:447–458
Cai YD, Feng KY, Lu WC, Chou KC (2006) Using LogitBoost classifier to predict protein structural classes. J Theor Biol 238:172–176
Calle ML, Urrea V (2010) Letter to the editor: stability of random forest importance measures. Brief Bioinform 12:86–89
Carmel Y, Paz S, Jahashan F, Shoshany M (2009) Assessing fire risk using Monte Carlo simulations of fire spread. For Ecol Manag 257:370–377
Carrara A, Guzzetti F (2013) Geographical information systems in assessing natural hazards vol 5. Springer Science & Business Media,
Catani F, Lagomarsino D, Segoni S, Tofani V (2013) Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues. Nat Hazards Earth Syst Sci 13:2815–2831
Chan JCW, Paelinckx D (2008) Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Remote Sens Environ 112:2999–3011. https://doi.org/10.1016/j.rse.2008.02.011
Chen W et al (2017) A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena 151:147–160
Chung CJ, Fabbri AG (2008) Predicting landslides for risk analysis—spatial models tested by a cross-validation technique. Geomorphology 94:438–452
Chuvieco E et al (2010) Development of a framework for fire risk assessment using remote sensing and geographic information system technologies. Ecol Model 221:46–58
Chuvieco E, Cocero D, Riano D, Martin P, Martınez-Vega J, de la Riva J, Perez F (2004) Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sens Environ 92:322–331
Cortez P, Morais AdJR (2007) A data mining approach to predict forest fires using meteorological data. In: Neves J, Santos MF, Machado J (eds) New trends in artificial intelligence. Proceedings of the 13th EPIA 2007 - Portuguese Conference on Artificial Intelligence, December, Guimaraes, Portugal. APPIA, pp 512–523
De Comité F, Gilleron R, Tommasi M Learning multi-label alternating decision trees from texts and data. In: Int Workshop Machine Learning Data Mining Pattern Recognition, 2003. Springer, pp 35–49
Dettling M, Bühlmann P (2003) Boosting for tumor classification with gene expression data. Bioinformatics 19:1061–1069
Drobyshev I, Niklasson M, Linderholm HW (2012) Forest fire activity in Sweden: climatic controls and geographical patterns in 20th century. Agric For Meteorol 154–155:174–186. https://doi.org/10.1016/j.agrformet.2011.11.002
FAO (2001) International handbook on forest fire protection. Technical guide for the countries of the Mediterranean basin. Division Agriculture et Forêt Méditerranéennes, Groupement d’Aix en Provence, France
Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann Stat 28:337–407
Gao X, Fei X, Xie H Forest fire risk zone evaluation based on high spatial resolution RS image in Liangyungang Huaguo Mountain scenic spot. In: Spatial Data Mining and Geographical Knowledge Services (ICSDM), 2011 IEEE International Conference on, 2011. IEEE, pp 593–596
Ghobadi GJ, Gholizadeh B, Dashliburun OM (2012) Forest fire risk zone mapping from geographic information system in northern forests of Iran (case study, Golestan province). Int J Agriculture Crop Sci 4:818–824
Gomez H, Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Eng Geol 78:11–27
Gonzalez-Olabarria JR, Brotons L, Gritten D, Tudela A, Teres JA (2012) Identifying location and causality of fire ignition hotspots in a Mediterranean region. Int J Wildland Fire 21:905–914
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
Hally B, Wallace L, Reinke K, Jones S (2016) Assessment of the utility of the advanced himawari imager to detect active fire over Australia International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences 41
Higuera PE, Abatzoglou JT, Littell JS, Morgan P (2015) The changing strength and nature of fire-climate relationships in the northern Rocky Mountains, USA, 1902-2008. PLoS One 10:e0127563
Hong H, Naghibi SA, Dashtpagerdi MM, Pourghasemi HR, Chen W (2017a) 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. Arab J Geosci 10:167
Hong H, Pradhan B, Bui DT, Xu C, Youssef AM, Chen W (2017b) Comparison of four kernel functions used in support vector machines for landslide susceptibility mapping: a case study at Sichuan area (China). Geomatics, Natural Hazards Risk 8:544–569
Hong H, Pradhan B, Xu C, Bui DT (2015) Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. Catena 133:266–281
Hosmer D, Lemeshow S (2000) Applied logistic regression 2nd edn Wiley-Interscience Publication. John Wiley, Hoboken, New Jersey,
Huebner K, Lindo Z, Lechowicz M (2012) Post-fire succession of collembolan communities in a northern hardwood forest. Eur J Soil Biol 48:59–65
Iba W, Langley P (1992) Induction of one-level decision trees. In: Machine learning proceedings 1992. Elsevier, pp 233–240
Jaakkola TS, Haussler D Probabilistic kernel regression models. In: AISTATS, 1999
Jahdi R et al (2014) Calibration of FARSITE fire area simulator in Iranian northern forests. Natural Hazards Earth System Sci Discussions 2:6201–6240
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 4:1–10
Jolly WM, Cochrane MA, Freeborn PH, Holden ZA, Brown TJ, Williamson GJ, Bowman DM (2015) Climate-induced variations in global wildfire danger from 1979 to 2013. Nat Commun 6
Kane VR et al (2015) Mixed severity fire effects within the Rim fire: relative importance of local climate, fire weather, topography, and forest structure. For Ecol Manag 358:62–79
Kantardzic M (2011) Data mining: concepts, models, methods, and algorithms. John Wiley & Sons, Hoboken, New Jersey
Kotsiantis SB, Zaharakis I, Pintelas P (2007) Supervised machine learning: a review of classification techniques emerging artificial intelligence applications in computer engineering 160:3–24
Lamsal P, Kumar L, Shabani F, Atreya K (2017) The greening of the Himalayas and Tibetan Plateau under climate change. Glob Planet Chang 159:77–92
Le TH, Nguyen TNT, Lasko K, Ilavajhala S, Vadrevu KP, Justice C (2014) Vegetation fires and air pollution in Vietnam. Environ Pollut 195:267–275
Lee S, Oh HJ (2012) Ensemble-based landslide susceptibility maps in Jinbu area, Korea. In: Terrigenous mass movements. Springer, pp 193–220
Lemaître G, Nogueira F, Aridas CK (2017) Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. J Machine Learning Res 18:559–563
Lin H, Liu X, Wang X, Liu Y (2018) A fuzzy inference and big data analysis algorithm for the prediction of forest fire based on rechargeable wireless sensor networks. Sustainable Computing: Informatics Systems 18:101–111. https://doi.org/10.1016/j.suscom.2017.05.004
Marjanovic M, Kovacevic M, Bajat B, Vozenílek V (2011) Landslide susceptibility assessment using SVM machine learning algorithm. Eng Geol 123:225–234. https://doi.org/10.1016/j.enggeo.2011.09.006
Martínez-Álvarez F, Reyes J, Morales-Esteban A, Rubio-Escudero C (2013) Determining the best set of seismicity indicators to predict earthquakes. Two case studies: Chile and the Iberian Peninsula. Knowl-Based Syst 50:198–210
Massada AB, Syphard AD, Stewart SI, Radeloff VC (2013) Wildfire ignition-distribution modelling: a comparative study in the Huron–Manistee National Forest, Michigan, USA. Int J Wildland Fire 22:174–183
Maudes J, Rodríguez JJ, García-Osorio C, García-Pedrajas N (2012) Random feature weights for decision tree ensemble construction. Information Fusion 13:20–30
Micheletti N, Foresti L, Robert S, Leuenberger M, Pedrazzini A, Jaboyedoff M, Kanevski M (2014) Machine learning feature selection methods for landslide susceptibility mapping. Math Geosci 46:33–57
Ministry of Agriculture and Rural Development of Vietnam (2016) The Vietnam’s FireWatch system for online monitoring and management of forest fires, http://www.kiemlam.org.vn/firewatchvn Ministry of Agriculture and Rural Development of Vietnam Accessed 12/4/2016 2016
Mojaddadi H, Pradhan B, Nampak H, Ahmad N, AHb G (2017) Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS. Geomatics, Natural Hazards Risk 8:1080–1102
Motazeh AG, Ashtiani EF, Baniasadi R, Choobar FM (2013) Rating and mapping fire hazard in the hardwood Hyrcanian forests using GIS and expert choice software Acknowledgement to reviewers of the manuscripts submitted to Forestry Ideas in 2013:141
Nami M, Jaafari A, Fallah M, Nabiuni S (2018) Spatial prediction of wildfire probability in the Hyrcanian ecoregion using evidential belief function model and GIS. Int J Environ Sci Technol 15:373–384
Nepstad DC, Stickler CM, Soares-Filho B, Merry F (2008) Interactions among Amazon land use, forests and climate: prospects for a near-term forest tipping point. Philosophical Trans Royal Soc B: Biological Sci 363:1737–1746
Ngoc-Thach N, Bao-Toan Ngo D, Xuan-Canh P, Hong-Thi N, Hang Thi B, Nhat-Duc H, Tien Bui D (2018) Spatial pattern assessment of tropical forest fire danger at Thuan Chau area (Vietnam) using GIS-based advanced machine learning algorithms: a comparative study. Eco Inform 46:74–85
North M, Stephens S, Collins B, Agee J, Aplet G, Franklin J, Fulé P (2015) Reform forest fire management. Science 349:1280–1281
Oliveira S, Oehler F, San-Miguel-Ayanz J, Camia A, Pereira JM (2012) Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest vol 275
Parisien MA, Snetsinger S, Greenberg JA, Nelson CR, Schoennagel T, Dobrowski SZ, Moritz MA (2012) Spatial variability in wildfire probability across the western United States. Int J Wildland Fire 21:313–327
Pausas JG, Belliure J, Mínguez E, Montagud S (2018) Fire benefits flower beetles in a Mediterranean ecosystem. PLoS One 13:e0198951
Pellegrini AF et al (2017) Convergence of bark investment according to fire and climate structures ecosystem vulnerability to future change. Ecol Lett 20:307–316
Perner P (2018) Machine learning and data mining in pattern recognition: 14th International Conference, MLDM 2018, New York, NY, USA, July 15–19, 2018, Proceedings vol 10935. Springer
Petropoulos GP, Vadrevu KP, Xanthopoulos G, Karantounias G, Scholze M (2010) A comparison of spectral angle mapper and artificial neural network classifiers combined with Landsat TM imagery analysis for obtaining burnt area mapping. Sensors 10:1967–1985
Pham BT, Bui DT, Dholakia M, Prakash I, Pham HV (2016) A comparative study of least square support vector machines and multiclass alternating decision trees for spatial prediction of rainfall-induced landslides in a tropical cyclones area. Geotech Geol Eng 34:1807–1824
Pham BT, Prakash I, Tien Bui D (2018a) Spatial prediction of landslides using a hybrid machine learning approach based on random subspace and classification and regression trees. Geomorphology 303:256–270
Pham BT, Tien Bui D, Prakash I (2018b) Bagging based Support Vector Machines for spatial prediction of landslides. Environ Earth Sci 77:146
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 31:80–98
Pourghasemi HR, Beheshtirad M, Pradhan B (2016) A comparative assessment of prediction capabilities of modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic models using Netcad-GIS for forest fire susceptibility mapping. Geomatics, Natural Hazards Risk 7:861–885
Pourghasemi HR, Pradhan B, Gokceoglu C (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63:965–996
Pourtaghi ZS, Pourghasemi HR, Rossi M (2015) Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran. Environ Earth Sci 73:1515–1533
Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–365. https://doi.org/10.1016/j.cageo.2012.08.023
Pradhan B, Dini Hairi Bin Suliman M, Arshad Bin Awang M (2007) Forest fire susceptibility and risk mapping using remote sensing and geographical information systems (GIS). Disaster Prevention Management: An Int J 16:344–352
Prasad VK, Badarinath K, Eaturu A (2008) Biophysical and anthropogenic controls of forest fires in the Deccan Plateau. India J Environ Management 86:1–13
Product GGD, Reserve NN, Areas PP (2002) Assessment of the special-use forest system and its management in Lao Cai Province
Ramirez-Cabral NYZ, Kumar L, Shabani F (2018) Suitable areas of Phakopsora pachyrhizi, Spodoptera exigua, and their host plant Phaseolus vulgaris are projected to reduce and shift due to climate change. Theor Appl Climatol:1–16
Ramirez-Cabral NYZ, Kumar L, Shabani F (2017) Global risk levels for corn rusts (Puccinia sorghi and Puccinia polysora) under climate change projections. J Phytopathol 165:563–574
Rodriguez JJ, Kuncheva LI, Alonso CJ (2006) Rotation forest: a new classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 28:1619–1630
Schapire RE, Singer Y (1999) Improved boosting algorithms using confidence-rated predictions. Mach Learn 37:297–336
Scholkopf B, Sung KK, Burges CJ, Girosi F, Niyogi P, Poggio T, Vapnik V (1997) Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Trans Signal Process 45:2758–2765
Setiawan I, Mahmud A, Mansor S, Mohamed Shariff A, Nuruddin A (2004) GIS-grid-based and multi-criteria analysis for identifying and mapping peat swamp forest fire hazard in Pahang, Malaysia. Disaster Prevention Management: An Int J 13:379–386
Shabani F, Kumar L, Ahmadi M (2017) Climate modelling shows increased risk to Eucalyptus sideroxylon on the Eastern Coast of Australia compared to Eucalyptus albens. Plants 6:58
Sheng VS, Gu B, Fang W, Wu J (2014) Cost-sensitive learning for defect escalation. Knowl-Based Syst 66:146–155
Skakun S, Kussul N, Shelestov AY, Lavreniuk M, Kussul O (2016) Efficiency assessment of multitemporal C-band Radarsat-2 intensity and Landsat-8 surface reflectance satellite imagery for crop classification in Ukraine. IEEE J Selected Topics Appl Earth Observations Remote Sensing 9:3712–3719
Sugumaran V, Muralidharan V, Ramachandran K (2007) Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing. Mech Syst Signal Process 21:930–942
Tehrany M, Jones S (2017) Evaluating the variations in the flood susceptibility maps accuracies due to the alterations in the type and extent of the flood inventory ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences:209–214
Tehrany MS, Kumar L, Drielsma MJ (2017) Review of native vegetation condition assessment concepts, methods and future trends. J Nat Conserv 40:12–23
Tehrany MS, Lee M-J, Pradhan B, Jebur MN, Lee S (2014a) Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environ Earth Sci 72:4001–4015
Tehrany MS, Pradhan B, Jebur MN (2013) Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. J Hydrol 504:69–79
Tehrany MS, Pradhan B, Jebur MN (2014b) Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J Hydrol 512:332–343
Tehrany MS, Pradhan B, Mansor S, Ahmad N (2015) Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena 125:91–101
Teodoro AC, Duarte L (2013) Forest fire risk maps: a GIS open source application–a case study in Norwest of Portugal. Int J Geogr Inf Sci 27:699–720
Tien Bui D, Pradhan B, Lofman O, Revhaug I (2012a) Landslide susceptibility assessment in vietnam using support vector machines, decision tree, and Naive Bayes Models Mathematical Problems in Engineering 2012
Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012b) Application of support vector machines in landslide susceptibility assessment for the Hoa Binh province (Vietnam) with kernel functions analysis
Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick ØB (2013) Regional prediction of landslide hazard using probability analysis of intense rainfall in the Hoa Binh province, Vietnam. Nat Hazards 66:707–730
Tien Bui D, Ho TC, Pradhan B, Pham BT, Nhu VH, Revhaug I (2016a) GIS-based modeling of rainfall-induced landslides using data mining based functional trees classifier with AdaBoost, bagging, and MultiBoost ensemble frameworks. Environ Earth Sci 75:1101–1123
Tien Bui D, Le K-TT, Nguyen VC, Le HD, Revhaug I (2016b) Tropical forest fire susceptibility mapping at the cat Ba national park area, Hai Phong City, Vietnam, using GIS-based kernel logistic regression. Remote Sens 8:347
Tien Bui D, Tuan TA, Klempe H, Pradhan B, Revhaug I (2016c) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13:361–378
Tien Bui D, Le HV, Hoang N-D (2018a) GIS-based spatial prediction of tropical forest fire danger using a new hybrid machine learning method. Eco Inform 48:104–116
Tien Bui D et al (2018b) Land subsidence susceptibility mapping in South Korea using machine learning algorithms. Sensors 18:2464
Trinh PT et al (2012) Late Quaternary tectonics and seismotectonics along the Red River fault zone, North Vietnam. Earth Sci Rev 114:224–235
Truong XL et al (2018) Enhancing prediction performance of landslide susceptibility model using hybrid machine learning approach of bagging ensemble and logistic model tree. Appl Sci 8:1046
Vafaei S, Soosani J, Adeli K, Fadaei H, Naghavi H, Pham TD, Tien Bui D (2018) Improving accuracy estimation of forest aboveground biomass based on incorporation of ALOS-2 PALSAR-2 and Sentinel-2A imagery and machine learning: a case study of the Hyrcanian forest area (Iran). Remote Sens 10:172
Valdes G, Luna JM, Eaton E, Simone CB II, Ungar LH, Solberg TD (2016) MediBoost: a patient stratification tool for interpretable decision making in the era of precision medicine. Sci Rep 6:37854
Verde J, Zêzere J (2010) Assessment and validation of wildfire susceptibility and hazard in Portugal. Nat Hazards Earth Syst Sci 10:485–497
Vilar L, Woolford DG, Martell DL, Martín MP (2010) A model for predicting human-caused wildfire occurrence in the region of Madrid, Spain. Int J Wildland Fire 19:325–337
Wallace L, Gupta V, Reinke K, Jones S (2016) An assessment of pre- and post fire near surface fuel hazard in an Australian dry sclerophyll forest using point cloud data captured using a terrestrial laser scanner. Remote Sens 8:679
Westerling AL, Turner MG, Smithwick EA, Romme WH, Ryan MG (2011) Continued warming could transform Greater Yellowstone fire regimes by mid-21st century. Proc Natl Acad Sci 108:13165–13170
Wickramasinghe CH, Jones S, Reinke K, Wallace L (2016) Development of a multi-spatial resolution approach to the surveillance of active fire lines using Himawari-8. Remote Sens 8:932
Wotton BM, Nock CA, Flannigan MD (2010) Forest fire occurrence and climate change in Canada. Int J Wildland Fire 19:253–271
Xiao J, Shen Y, Ge J, Tateishi R, Tang C, Liang Y, Huang Z (2006) Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing. Landsc Urban Plan 75:69–80
Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat—Turkey). Comput Geosci 35:1125–1138