A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data

Abstract

A reliable forest fire susceptibility map is a necessity for disaster management and a primary reference source in land use planning. We set out to evaluate the use of the LogitBoost ensemble-based decision tree (LEDT) machine learning method for forest fire susceptibility mapping through a comparative case study at the Lao Cai region of Vietnam. A thorough literature search would indicate the method has not previously been applied to forest fires. Support vector machine (SVM), random forest (RF), and Kernel logistic regression (KLR) were used as benchmarks in the comparative evaluation. A fire inventory database for the study area was constructed based on data of previous forest fire occurrences, and related conditioning factors were generated from a number of sources. Thereafter, forest fire probability indices were computed through each of the four modeling techniques, and performances were compared using the area under the curve (AUC), Kappa index, overall accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV). The LEDT model produced the best performance, both on the training and on validation datasets, demonstrating a 92% prediction capability. Its overall superiority over the benchmarking models suggests that it has the potential to be used as an efficient new tool for forest fire susceptibility mapping. Fire prevention is a critical concern for local forestry authorities in tropical Lao Cai region, and based on the evidence of our study, the method has a potential application in forestry conservation management.

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References

  1. 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

    Article  Google Scholar 

  2. 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

    Article  Google Scholar 

  3. 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

    Article  Google Scholar 

  4. 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

    Article  Google Scholar 

  5. 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

    Article  Google Scholar 

  6. 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

    Article  Google Scholar 

  7. 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

    Article  Google Scholar 

  8. 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

  9. Breiman L (1996) Bagging predictors. Mach Learn 24:123–140

    Google Scholar 

  10. Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  11. 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

    Article  Google Scholar 

  12. Cai YD, Feng KY, Lu WC, Chou KC (2006) Using LogitBoost classifier to predict protein structural classes. J Theor Biol 238:172–176

    Article  Google Scholar 

  13. Calle ML, Urrea V (2010) Letter to the editor: stability of random forest importance measures. Brief Bioinform 12:86–89

    Article  Google Scholar 

  14. 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

    Article  Google Scholar 

  15. Carrara A, Guzzetti F (2013) Geographical information systems in assessing natural hazards vol 5. Springer Science & Business Media,

  16. 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

    Article  Google Scholar 

  17. 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

    Article  Google Scholar 

  18. 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

    Article  Google Scholar 

  19. Chung CJ, Fabbri AG (2008) Predicting landslides for risk analysis—spatial models tested by a cross-validation technique. Geomorphology 94:438–452

    Article  Google Scholar 

  20. 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

    Article  Google Scholar 

  21. 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

    Article  Google Scholar 

  22. Cloke H, Pappenberger F (2009) Ensemble flood forecasting: a review. J Hydrol 375:613–626

    Article  Google Scholar 

  23. 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

  24. 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

  25. Dettling M, Bühlmann P (2003) Boosting for tumor classification with gene expression data. Bioinformatics 19:1061–1069

    Article  Google Scholar 

  26. 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

    Article  Google Scholar 

  27. 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

  28. 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

    Article  Google Scholar 

  29. 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

  30. 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

    Google Scholar 

  31. Gomez H, Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Eng Geol 78:11–27

    Article  Google Scholar 

  32. 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

    Article  Google Scholar 

  33. 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

    Article  Google Scholar 

  34. 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

  35. 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

    Article  Google Scholar 

  36. 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

    Article  Google Scholar 

  37. 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

    Article  Google Scholar 

  38. 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

    Article  Google Scholar 

  39. Hosmer D, Lemeshow S (2000) Applied logistic regression 2nd edn Wiley-Interscience Publication. John Wiley, Hoboken, New Jersey,

  40. 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

    Article  Google Scholar 

  41. Iba W, Langley P (1992) Induction of one-level decision trees. In: Machine learning proceedings 1992. Elsevier, pp 233–240

  42. Jaakkola TS, Haussler D Probabilistic kernel regression models. In: AISTATS, 1999

  43. Jahdi R et al (2014) Calibration of FARSITE fire area simulator in Iranian northern forests. Natural Hazards Earth System Sci Discussions 2:6201–6240

    Article  Google Scholar 

  44. 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

    Article  Google Scholar 

  45. 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

  46. 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

    Article  Google Scholar 

  47. Kantardzic M (2011) Data mining: concepts, models, methods, and algorithms. John Wiley & Sons, Hoboken, New Jersey

    Book  Google Scholar 

  48. 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

  49. 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

    Article  Google Scholar 

  50. 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

    Article  Google Scholar 

  51. Lee S, Oh HJ (2012) Ensemble-based landslide susceptibility maps in Jinbu area, Korea. In: Terrigenous mass movements. Springer, pp 193–220

  52. 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

    Google Scholar 

  53. 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

    Article  Google Scholar 

  54. 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

    Article  Google Scholar 

  55. 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

    Article  Google Scholar 

  56. 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

    Article  Google Scholar 

  57. Maudes J, Rodríguez JJ, García-Osorio C, García-Pedrajas N (2012) Random feature weights for decision tree ensemble construction. Information Fusion 13:20–30

    Article  Google Scholar 

  58. 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

    Article  Google Scholar 

  59. 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

  60. 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

    Article  Google Scholar 

  61. 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

  62. 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

    Article  Google Scholar 

  63. 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

    Article  Google Scholar 

  64. 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

    Article  Google Scholar 

  65. North M, Stephens S, Collins B, Agee J, Aplet G, Franklin J, Fulé P (2015) Reform forest fire management. Science 349:1280–1281

    Article  Google Scholar 

  66. 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

  67. 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

    Article  Google Scholar 

  68. Pausas JG, Belliure J, Mínguez E, Montagud S (2018) Fire benefits flower beetles in a Mediterranean ecosystem. PLoS One 13:e0198951

    Article  Google Scholar 

  69. 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

    Article  Google Scholar 

  70. 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

  71. 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

    Article  Google Scholar 

  72. 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

    Article  Google Scholar 

  73. 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

    Article  Google Scholar 

  74. Pham BT, Tien Bui D, Prakash I (2018b) Bagging based Support Vector Machines for spatial prediction of landslides. Environ Earth Sci 77:146

    Article  Google Scholar 

  75. 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

    Article  Google Scholar 

  76. 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

    Article  Google Scholar 

  77. 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

    Article  Google Scholar 

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

    Article  Google Scholar 

  79. 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

    Article  Google Scholar 

  80. 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

    Article  Google Scholar 

  81. 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

    Article  Google Scholar 

  82. Product GGD, Reserve NN, Areas PP (2002) Assessment of the special-use forest system and its management in Lao Cai Province

  83. 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

  84. 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

    Article  Google Scholar 

  85. Rodriguez JJ, Kuncheva LI, Alonso CJ (2006) Rotation forest: a new classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 28:1619–1630

    Article  Google Scholar 

  86. Schapire RE, Singer Y (1999) Improved boosting algorithms using confidence-rated predictions. Mach Learn 37:297–336

    Article  Google Scholar 

  87. 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

    Article  Google Scholar 

  88. 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

    Article  Google Scholar 

  89. 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

    Article  Google Scholar 

  90. Sheng VS, Gu B, Fang W, Wu J (2014) Cost-sensitive learning for defect escalation. Knowl-Based Syst 66:146–155

    Article  Google Scholar 

  91. 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

    Article  Google Scholar 

  92. 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

    Article  Google Scholar 

  93. 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

  94. Tehrany MS, Kumar L, Drielsma MJ (2017) Review of native vegetation condition assessment concepts, methods and future trends. J Nat Conserv 40:12–23

    Article  Google Scholar 

  95. 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

    Article  Google Scholar 

  96. 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

    Article  Google Scholar 

  97. 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

    Article  Google Scholar 

  98. 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

    Article  Google Scholar 

  99. 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

    Article  Google Scholar 

  100. 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

  101. 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

  102. 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

    Article  Google Scholar 

  103. 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

    Article  Google Scholar 

  104. 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

    Article  Google Scholar 

  105. 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

    Article  Google Scholar 

  106. 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

    Article  Google Scholar 

  107. Tien Bui D et al (2018b) Land subsidence susceptibility mapping in South Korea using machine learning algorithms. Sensors 18:2464

    Article  Google Scholar 

  108. Trinh PT et al (2012) Late Quaternary tectonics and seismotectonics along the Red River fault zone, North Vietnam. Earth Sci Rev 114:224–235

    Article  Google Scholar 

  109. 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

    Article  Google Scholar 

  110. 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

    Article  Google Scholar 

  111. 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

    Article  Google Scholar 

  112. Verde J, Zêzere J (2010) Assessment and validation of wildfire susceptibility and hazard in Portugal. Nat Hazards Earth Syst Sci 10:485–497

    Article  Google Scholar 

  113. 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

    Article  Google Scholar 

  114. 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

    Article  Google Scholar 

  115. Webb GI (2000) Multiboosting: a technique for combining boosting and wagging. Mach Learn 40:159–196

    Article  Google Scholar 

  116. 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

    Article  Google Scholar 

  117. 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

    Article  Google Scholar 

  118. Wotton BM, Nock CA, Flannigan MD (2010) Forest fire occurrence and climate change in Canada. Int J Wildland Fire 19:253–271

    Article  Google Scholar 

  119. 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

    Article  Google Scholar 

  120. 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

    Article  Google Scholar 

  121. Yuan C, Zhang Y, Liu Z (2015) A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques. Can J For Res 45:783–792

    Article  Google Scholar 

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Acknowledgements

We would like to greatly thank the following institutions for providing the data for this research: (1) Ministry of Agriculture and Rural Development (Vietnam), (2) Ministry of Natural Resources and Environment (Vietnam), (3) NOAA’s National Centers for Environmental Information (USA), and (4) U.S. Geological Survey.

Funding

This research was supported by the Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam.

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Correspondence to Dieu Tien Bui.

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Tehrany, M.S., Jones, S., Shabani, F. et al. A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data. Theor Appl Climatol 137, 637–653 (2019). https://doi.org/10.1007/s00704-018-2628-9

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