Advertisement

Application of rotation forest with decision trees as base classifier and a novel ensemble model in spatial modeling of groundwater potential

  • Seyed Amir NaghibiEmail author
  • Mojtaba Dolatkordestani
  • Ashkan Rezaei
  • Payam Amouzegari
  • Mostafa Taheri Heravi
  • Bahareh Kalantar
  • Biswajeet Pradhan
Article
  • 136 Downloads

Abstract

Groundwater resources are facing a high pressure due to drought and overexploitation. The main aim of this research is to apply rotation forest (RTF) with decision trees as base classifiers and an improved ensemble methodology based on evidential belief function and tree-based models (EBFTM) for preparing groundwater potential maps (GPM). The performance of these new models is then compared with three previously implemented models, i.e., boosted regression tree (BRT), classification and regression tree (CART), and random forest (RF). For this purpose, spring locations in the Meshgin Shahr in Iran were detected. The spring locations were randomly categorized into training (70% of the locations) and validation (30% of the locations) datasets. Furthermore, several groundwater conditioning factors (GCFs) such as hydrogeological, topographical, and land use factors were mapped and regarded as input variables. The tree-based algorithms (i.e., BRT, CART, RF, and RTF) were applied by implementing the input variables and training dataset. The groundwater potential values (i.e., spring occurrence probability) obtained by the BRT, CART, RF, and RTF models for all the pixels of the study area were classified into four potential classes and then used as inputs of the EBF model to construct the new ensemble model (i.e., EBFTM). At last, this paper implemented a receiver operating characteristics (ROC) curve for determining the efficiency of the EBFTM, RTF, BRT, CART, and RF methods. The findings illustrated that the EBFTM had the highest efficacy with an area under the ROC curve (AUC) of 90.4%, followed by the RF, BRT, CART, and RTF models with AUC-ROC values of 90.1, 89.8, 86.9, and 86.2%, respectively. Thus, it could be inferred that the ensemble approach is capable of improving the efficacy of the single tree-based models in GPM production.

Keywords

Hydrogeology Water resource management GIS Spatial modeling Data mining 

Notes

References

  1. Adiat, K. A. N., Nawawi, M. N. M., & Abdullah, K. (2012). Assessing the accuracy of GIS-based elementary multi criteria decision analysis as a spatial prediction tool—a case of predicting potential zones of sustainable groundwater resources. Journal of Hydrology, 440–441, 75–89.  https://doi.org/10.1016/J.JHYDROL.2012.03.028.CrossRefGoogle Scholar
  2. Aertsen, W., Kint, V., van Orshoven, J., Özkan, K., & Muys, B. (2010). Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests. Ecological Modelling, 221(8), 1119–1130.  https://doi.org/10.1016/J.ECOLMODEL.2010.01.007.CrossRefGoogle Scholar
  3. Aghdam, I. N., Varzandeh, M. H. M., & Pradhan, B. (2016). Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran). Environmental Earth Sciences, 75(7), 553.  https://doi.org/10.1007/s12665-015-5233-6.CrossRefGoogle Scholar
  4. Aličković, E., & Subasi, A. (2017). Breast cancer diagnosis using GA feature selection and rotation forest. Neural Computing and Applications, 28(4), 753–763.  https://doi.org/10.1007/s00521-015-2103-9.CrossRefGoogle Scholar
  5. Althuwaynee, O. F., Pradhan, B., Park, H.-J., & Lee, J. H. (2014). A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena, 114, 21–36.  https://doi.org/10.1016/j.catena.2013.10.011.CrossRefGoogle Scholar
  6. Asia, S., & Richman, P. (1991). Planning for groundwater protection, 273.  https://doi.org/10.1016/S0376-7361(09)70018-4.
  7. Bartels, L. (1997). Specification uncertainty and model averaging. American Journal of Political Science, 41(2), 641–674.  https://doi.org/10.2307/2111781.CrossRefGoogle Scholar
  8. Beven, K. (1997). TOPMODEL: a critique. Hydrological Process, 11, 1069–1085.CrossRefGoogle Scholar
  9. Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.  https://doi.org/10.1023/A:1010933404324.CrossRefGoogle Scholar
  10. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Wadsworth and Brooks-Cole Advanced Books and Software. California: Pacific Grove.Google Scholar
  11. Carranza, E. J. M., van Ruitenbeek, F. J. a., Hecker, C., van der Meijde, M., & van der Meer, F. D. (2008). Knowledge-guided data-driven evidential belief modeling of mineral prospectivity in Cabo de Gata, SE Spain. International Journal of Applied Earth Observation and Geoinformation, 10(3), 374–387.  https://doi.org/10.1016/j.jag.2008.02.008.CrossRefGoogle Scholar
  12. Carty, D. (2011). An analysis of boosted regression trees to predict the strength properties of wood composites. Masters Theses. http://trace.tennessee.edu/utk_gradthes/954. Accessed 5 April 2018.
  13. Caruana, R., & Niculescu-Mizil, A. (2006). An empirical comparison of supervised learning algorithms. In Proceedings of the 23rd international conference on Machine learning - ICML ‘06 (pp. 161–168). New York. New York: ACM Press.  https://doi.org/10.1145/1143844.1143865.CrossRefGoogle Scholar
  14. Chen, W., Pourghasemi, H. R., & Naghibi, S. A. (2017a). A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China. Bulletin of Engineering Geology and the Environment, 77, 1–18.  https://doi.org/10.1007/s10064-017-1010-y.CrossRefGoogle Scholar
  15. Chen, W., Pourghasemi, H. R., & Naghibi, S. A. (2017b). Prioritization of landslide conditioning factors and its spatial modeling in Shangnan County, China using GIS-based data mining algorithms. Bulletin of Engineering Geology and the Environment., 77, 611–629.  https://doi.org/10.1007/s10064-017-1004-9.CrossRefGoogle Scholar
  16. Chen, W., Shirzadi, A., Shahabi, H., Ahmad, B. B., Zhang, S., Hong, H., & Zhang, N. (2017c). A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China. Geomatics, Natural Hazards and Risk, 8(2), 1955–1977.  https://doi.org/10.1080/19475705.2017.1401560.CrossRefGoogle Scholar
  17. Chen, W., Li, H., Hou, E., Wang, S., Wang, G., Panahi, M., Li, T., Peng, T., Guo, C., Niu, C., Xiao, L., Wang, J., Xie, X., & Ahmad, B. B. (2018a). GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models. Science of the Total Environment, 634, 853–867.  https://doi.org/10.1016/J.SCITOTENV.2018.04.055.CrossRefGoogle Scholar
  18. Chen, W., Peng, J., Hong, H., Shahabi, H., Pradhan, B., Liu, J., Zhu, A. X., Pei, X., & Duan, Z. (2018b). Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. Science of the Total Environment, 626, 1121–1135.  https://doi.org/10.1016/J.SCITOTENV.2018.01.124.CrossRefGoogle Scholar
  19. Chen, W., Shahabi, H., Shirzadi, A., Li, T., Guo, C., Hong, H., Li, W., Pan, D., Hui, J., Ma, M., Xi, M., & Bin Ahmad, B. (2018c). A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment. Geocarto International, 33, 1–23.  https://doi.org/10.1080/10106049.2018.1425738.CrossRefGoogle Scholar
  20. Chen, W., Xie, X., Peng, J., Shahabi, H., Hong, H., Bui, D. T., et al. (2018d). GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method. CATENA, 164, 135–149.  https://doi.org/10.1016/J.CATENA.2018.01.012.CrossRefGoogle Scholar
  21. Chezgi, J., Pourghasemi, H. R., Naghibi, S. A., Moradi, H. R., & Kheirkhah Zarkesh, M. (2016). Assessment of a spatial multi-criteria evaluation to site selection underground dams in the Alborz Province, Iran. Geocarto International, 31(6), 628–646.  https://doi.org/10.1080/10106049.2015.1073366.CrossRefGoogle Scholar
  22. Dempster, A. P. (1968). A generalization of Bayesian inference. Journal of the Royal Statistical Society. Series B (Methodological). WileyRoyal Statistical Society.  https://doi.org/10.2307/2984504.
  23. Elith, J., & Leathwick, J. (2013). Boosted regression trees for ecological modeling (pp. 1–22).Google Scholar
  24. Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. The Journal of Animal Ecology, 77(4), 802–813.  https://doi.org/10.1111/j.1365-2656.2008.01390.x.CrossRefGoogle Scholar
  25. Geology Survey of Iran (GSI) (1997) http://www.gsi.ir/Main/Lang_ en/index.html
  26. Ghorbani Nejad, S., Falah, F., Daneshfar, M., Haghizadeh, A., & Rahmati, O. (2016). Delineation of groundwater potential zones using remote sensing and GIS-based data-driven models. Geocarto International, 1–21.  https://doi.org/10.1080/10106049.2015.1132481.
  27. Golkarian, A., Naghibi, S. A., Kalantar, B., & Pradhan, B. (2018). Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS, (190:149).  https://doi.org/10.1007/s10661-018-6507-8
  28. Hong, H., Naghibi, S. A., Pourghasemi, H. R., & Pradhan, B. (2016a). GIS-based landslide spatial modeling in Ganzhou City, China. Arabian Journal of Geosciences, 9(2), 112.  https://doi.org/10.1007/s12517-015-2094-y.CrossRefGoogle Scholar
  29. Hong, H., Pourghasemi, H. R., & Pourtaghi, Z. S. (2016b). Landslide susceptibility assessment in Lianhua County (China): a comparison between a random forest data mining technique and bivariate and multivariate statistical models. Geomorphology, 259, 105–118.  https://doi.org/10.1016/J.GEOMORPH.2016.02.012.CrossRefGoogle Scholar
  30. Hong, H., Naghibi, S. A., Moradi Dashtpagerdi, M., Pourghasemi, H. R. H. R., & 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. Arabian Journal of Geosciences, 10(7).  https://doi.org/10.1007/s12517-017-2905-4.
  31. Hong, H., Tsangaratos, P., Ilia, I., Chen, W., & Xu, C. (2017b). Comparing the performance of a logistic regression and a random Forest model in landslide susceptibility assessments. the case of Wuyaun area, China. In Advancing culture of living with landslides (pp. 1043–1050). Cham: Springer International Publishing.  https://doi.org/10.1007/978-3-319-53498-5_118.CrossRefGoogle Scholar
  32. Hong, H., Liu, J., Bui, D. T., Pradhan, B., Acharya, T. D., Pham, B. T., et al. (2018). Landslide susceptibility mapping using J48 decision tree with AdaBoost, bagging and rotation forest ensembles in the Guangchang area (China). CATENA, 163, 399–413. doi: https://doi.org/10.1016/J.CATENA.2018.01.005.CrossRefGoogle Scholar
  33. Hong, H., Miao, Y., Liu, J., & Zhu, A.-X. (2019). Exploring the effects of the design and quantity of absence data on the performance of random forest-based landslide susceptibility mapping. CATENA, 176, 45–64.  https://doi.org/10.1016/J.CATENA.2018.12.035.CrossRefGoogle Scholar
  34. Khorasan Razavi Regional Water Authority (2015). http://www.khrw.ir/?l=EN.
  35. Khosravi, K., Pham, B. T., Chapi, K., Shirzadi, A., Shahabi, H., Revhaug, I., Prakash, I., & Tien Bui, D. (2018a). A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Science of the Total Environment, 627, 744–755.  https://doi.org/10.1016/j.scitotenv.2018.01.266.CrossRefGoogle Scholar
  36. Khosravi, K., Sartaj, M., Tsai, F. T.-C., Singh, V. P., Kazakis, N., Melesse, A. M., Prakash, I., Tien Bui, D., & Pham, B. T. (2018b). A comparison study of DRASTIC methods with various objective methods for groundwater vulnerability assessment. Science of the Total Environment, 642, 1032–1049.  https://doi.org/10.1016/J.SCITOTENV.2018.06.130s.CrossRefGoogle Scholar
  37. Kordestani, M. D., Naghibi, S. A., Hashemi, H., Ahmadi, K., Kalantar, B., & Pradhan, B. (2018). Groundwater potential mapping using a novel data-mining ensemble model. Hydrogeology Journal, 27(1), 211–214.  https://doi.org/10.1007/s10040-018-1848-5.CrossRefGoogle Scholar
  38. Koyuncu, H., & Ceylan, R. (2013). Artificial neural network based on rotation forest for biomedical pattern classification. In 2013 36th International Conference on Telecommunications and Signal Processing (TSP) (pp. 581–585). IEEE.  https://doi.org/10.1109/TSP.2013.6614001.
  39. Lee, M.-J., Choi, J.-W., Oh, H.-J., Won, J.-S., Park, I., & Lee, S. (2012a). Ensemble-based landslide susceptibility maps in Jinbu area, Korea. Environmental Earth Sciences, 67(1), 23–37.  https://doi.org/10.1007/s12665-011-1477-y.CrossRefGoogle Scholar
  40. Lee, S., Kim, Y. S., & Oh, H. J. (2012b). Application of a weights-of-evidence method and GIS to regional groundwater productivity potential mapping. Journal of Environmental Management, 96(1), 91–105.  https://doi.org/10.1016/j.jenvman.2011.09.016.CrossRefGoogle Scholar
  41. Lu, H., Yang, L., Yan, K., Xue, Y., & Gao, Z. (2017). A cost-sensitive rotation forest algorithm for gene expression data classification. Neurocomputing, 228, 270–276.  https://doi.org/10.1016/J.NEUCOM.2016.09.077.CrossRefGoogle Scholar
  42. Manap, M. A., Nampak, H., Pradhan, B., Lee, S., Sulaiman, W. N. A., & Ramli, M. F. (2014). Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS. Arabian Journal of Geosciences, 7(2), 711–724.  https://doi.org/10.1007/s12517-012-0795-z.CrossRefGoogle Scholar
  43. McKenney, D. W., & Pedlar, J. H. (2003). Spatial models of site index based on climate and soil properties for two boreal tree species in Ontario, Canada. Forest Ecology and Management, 175(1–3), 497–507.  https://doi.org/10.1016/S0378-1127(02)00186-X.CrossRefGoogle Scholar
  44. Micheletti, N., Foresti, L., Robert, S., Leuenberger, M., Pedrazzini, A., Jaboyedoff, M., & Kanevski, M. (2013). Machine learning feature selection methods for landslide susceptibility mapping. Mathematical Geosciences, 46(1), 33–57.  https://doi.org/10.1007/s11004-013-9511-0.CrossRefGoogle Scholar
  45. Moore, I. D., & Burch, G. J. (1986). Sediment transport capacity ofsheet and rill flow: application of unit stream power theory. Water Research, 22(8), 1350–1360.Google Scholar
  46. Moore, I. D., Grayson, R. B., & Ladson, A. R. (1991). Digital terrain modeling: a review of hydrological, geomorphological and biological applications. Hydrological Processes, 5, 3–30.  https://doi.org/10.1002/hyp.3360050103.CrossRefGoogle Scholar
  47. Mousavi, S., Golkarian, A., Amir Naghibi, S., Kalantar, B., & Pradhan, B. (2017). GIS-based groundwater spring potential mapping using data mining boosted regression tree and probabilistic frequency ratio models in Iran. AIMS Geosciences, 3(1), 91–115.  https://doi.org/10.3934/geosci.2017.1.91.CrossRefGoogle Scholar
  48. Naghibi, S. A., Pourghasemi, H. R., Pourtaghi, Z. S., & Rezaei, A. (2015). Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran. Earth Science Informatics, 8(1), 1–16.  https://doi.org/10.1007/s12145-014-0145-7.CrossRefGoogle Scholar
  49. Naghibi, S. A., & Moradi Dashtpagerdi, M. (2016). Evaluation of four supervised learning methods for groundwater spring potential mapping in Khalkhal region (Iran) using GIS-based features. Hydrogeology Journal, 25, 169–189.  https://doi.org/10.1007/s10040-016-1466-z.CrossRefGoogle Scholar
  50. Naghibi, S. A., & Pourghasemi, H. R. (2015). A comparative assessment between three machine learning models and their performance comparison by bivariate and multivariate statistical methods in groundwater potential mapping. Water Resources Management, 29(14), 5217–5236.  https://doi.org/10.1007/s11269-015-1114-8.CrossRefGoogle Scholar
  51. Naghibi, S. A., Pourghasemi, H. R., & Dixon, B. (2016). GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environmental Monitoring and Assessment, 188(1), 44.  https://doi.org/10.1007/s10661-015-5049-6.CrossRefGoogle Scholar
  52. Naghibi, S. A., Moghaddam, D. D., Kalantar, B., Pradhan, B., & Kisi, O. (2017a). A comparative assessment of GIS-based data mining models and a novel ensemble model in groundwater well potential mapping. Journal of Hydrology, 548, 471–483.  https://doi.org/10.1016/j.jhydrol.2017.03.020.CrossRefGoogle Scholar
  53. Naghibi, S. A., Ahmadi, K., & Daneshi, A. (2017b). Application of support vector machine, random Forest, and genetic algorithm optimized random Forest models in groundwater potential mapping. Water Resources Management, 31(9), 1–15.  https://doi.org/10.1007/s11269-017-1660-3.CrossRefGoogle Scholar
  54. Naghibi, S. A., Pourghasemi, H. R., & Abbaspour, K. (2018a). A comparison between ten advanced and soft computing models for groundwater qanat potential assessment in Iran using R and GIS. Theoretical and Applied Climatology, 131(3–4), 967–984.  https://doi.org/10.1007/s00704-016-2022-4.CrossRefGoogle Scholar
  55. Naghibi, S. A., Vafakhah, M., Hashemi, H., Pradhan, B., & Alavi, S. (2018b). Groundwater augmentation through the site selection of floodwater spreading using a data mining approach (case study: Mashhad plain, Iran). Water, 10(10), 1405.  https://doi.org/10.3390/w10101405.CrossRefGoogle Scholar
  56. Nampak, H., Pradhan, B., & Manap, M. A. (2014). Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. Journal of Hydrology, 513, 283–300.  https://doi.org/10.1016/j.jhydrol.2014.02.053.CrossRefGoogle Scholar
  57. Oh, H.-J., Kim, Y.-S., Choi, J.-K., Park, E., & Lee, S. (2011). GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea. Journal of Hydrology, 399(3–4), 158–172.  https://doi.org/10.1016/j.jhydrol.2010.12.027.CrossRefGoogle Scholar
  58. Ozdemir, A. (2011a). GIS-based groundwater spring potential mapping in the Sultan Mountains (Konya, Turkey) using frequency ratio, weights of evidence and logistic regression methods and their comparison. Journal of Hydrology, 411(3–4), 290–308.  https://doi.org/10.1016/j.jhydrol.2011.10.010.CrossRefGoogle Scholar
  59. Ozdemir, A. (2011b). Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey). Journal of Hydrology, 405(1–2), 123–136.  https://doi.org/10.1016/j.jhydrol.2011.05.015.CrossRefGoogle Scholar
  60. Pardo, M., & Sberveglieri, G. (2008). Random forests and nearest shrunken centroids for the classification of sensor array data. Sensors and Actuators, B: Chemical, 131(1), 93–99.  https://doi.org/10.1016/j.snb.2007.12.015.CrossRefGoogle Scholar
  61. Park, I., Lee, J., & Saro, L. (2014). Ensemble of ground subsidence hazard maps using fuzzy logic. Central European Journal of Geosciences, 6(2), 207–218.  https://doi.org/10.2478/s13533-012-0175-y.CrossRefGoogle Scholar
  62. Peters, J., De Baets, B., Verhoest, N. E. C., Samson, R., Degroeve, S., De Becker, P., & Huybrechts, W. (2007). Random forests as a tool for ecohydrological distribution modelling. Ecological Modelling, 207(2–4), 304–318.  https://doi.org/10.1016/J.ECOLMODEL.2007.05.011.CrossRefGoogle Scholar
  63. Pham, B. T., Tien Bui, D., Prakash, I., & Dholakia, M. B. (2017). Hybrid integration of multilayer perceptron neural networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. CATENA, 149, 52–63.  https://doi.org/10.1016/J.CATENA.2016.09.007.CrossRefGoogle Scholar
  64. Pham, B. T. (2018). A novel classifier based on composite hyper-cubes on iterated random projections for assessment of landslide susceptibility. Journal of the Geological Society of India, 91(3), 355–362.  https://doi.org/10.1007/s12594-018-0862-5s.CrossRefGoogle Scholar
  65. Pham, B. T., & Prakash, I. (2018). Machine learning methods of kernel logistic regression and classification and regression trees for landslide susceptibility assessment at part of Himalayan area, India. Indian Journal of Science and Technology, 11(12), 1–10.  https://doi.org/10.17485/ijst/2018/v11i12/99745.CrossRefGoogle Scholar
  66. Pham, B. T., Shirzadi, A., Tien Bui, D., Prakash, I., & Dholakia, M. B. (2018a). A hybrid machine learning ensemble approach based on a radial basis function neural network and rotation forest for landslide susceptibility modeling: a case study in the Himalayan area, India. International Journal of Sediment Research, 33(2), 157–170.  https://doi.org/10.1016/J.IJSRC.2017.09.008.CrossRefGoogle Scholar
  67. Pham, B. T., Tien Bui, D., & Prakash, I. (2018b). Bagging based support vector machines for spatial prediction of landslides. Environmental Earth Sciences, 77(4), 146.  https://doi.org/10.1007/s12665-018-7268-y.CrossRefGoogle Scholar
  68. Pham, B. T., Prakash, I., & Tien Bui, D. (2018c). Spatial prediction of landslides using a hybrid machine learning approach based on random subspace and classification and regression trees. Geomorphology, 303, 256–270.  https://doi.org/10.1016/J.GEOMORPH.2017.12.008.CrossRefGoogle Scholar
  69. Pham, B. T., Jaafari, A., Prakash, I., & Bui, D. T. (2018d). A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling. Bulletin of Engineering Geology and the Environment, 1–22.  https://doi.org/10.1007/s10064-018-1281-y.
  70. Pham, B. T., Prakash, I., Khosravi, K., Chapi, K., Trinh, P. T., Ngo, T. Q., Hosseini, S. V., & Bui, D. T. (2018e). A comparison of support vector machines and Bayesian algorithms for landslide susceptibility modelling. Geocarto International, 1–23.  https://doi.org/10.1080/10106049.2018.1489422.
  71. Pourtaghi, Z. S., & Pourghasemi, H. R. (2014). GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province, Iran. Hydrogeology Journal, 22, 643–662.  https://doi.org/10.1007/s10040-013-1089-6.CrossRefGoogle Scholar
  72. Pradhan, B., Abokharima, M. H., Jebur, M. N., & Tehrany, M. S. (2014). Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Natural Hazards., 73, 1019–1042.  https://doi.org/10.1007/s11069-014-1128-1.CrossRefGoogle Scholar
  73. Prasad, V. K., Badarinath, K. V. S., & Eaturu, A. (2008). Biophysical and anthropogenic controls of forest fires in the Deccan Plateau, India. Journal of Environmental Management, 86(1), 1–13.  https://doi.org/10.1016/J.JENVMAN.2006.11.017.CrossRefGoogle Scholar
  74. Rahmati, O., Nazari Samani, A., Mahdavi, M., Pourghasemi, H. R., & Zeinivand, H. (2014). Groundwater potential mapping at Kurdistan region of Iran using analytic hierarchy process and GIS. Arabian Journal of Geosciences, 8(February 2016), 7059–7071.  https://doi.org/10.1007/s12517-014-1668-4.CrossRefGoogle Scholar
  75. Rahmati, O., & Melesse, A. M. (2016). Application of Dempster–Shafer theory, spatial analysis and remote sensing for groundwater potentiality and nitrate pollution analysis in the semi-arid region of Khuzestan, Iran, (June). doi: https://doi.org/10.1016/j.scitotenv.2016.06.176, 568, 1110, 1123.CrossRefGoogle Scholar
  76. Rahmati, O., Pourghasemi, H. R., & Melesse, A. M. (2016). Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: a case study at Mehran region, Iran. Catena, 137(October), 360–372.  https://doi.org/10.1016/j.catena.2015.10.010.CrossRefGoogle Scholar
  77. Rahmati, O., Naghibi, S. A., Shahabi, H., Bui, D. T., Pradhan, B., Azareh, A., Rafiei-Sardooi, E., Samani, A. N., & Melesse, A. M. (2018). Groundwater spring potential modelling: comprising the capability and robustness of three different modeling approaches. Journal of Hydrology, 565, 248–261.  https://doi.org/10.1016/J.JHYDROL.2018.08.027.CrossRefGoogle Scholar
  78. Ripley, B (2015). Package “rpart.”Google Scholar
  79. Rodriguez, J. J., Kuncheva, L. I., & Alonso, C. J. (2007). Rotation forest: a new classifier ensemble method. Pattern Analysis Machine Intelligence IEEE Transactions, 28, 1619–1630.  https://doi.org/10.1109/TPAMI.2006.211.CrossRefGoogle Scholar
  80. Sangchini, E. K., Emami, S. N., Tahmasebipour, N., Pourghasemi, H. R., Naghibi, S. A., Arami, S. A., & Pradhan, B. (2016). Assessment and comparison of combined bivariate and AHP models with logistic regression for landslide susceptibility mapping in the Chaharmahal-e-Bakhtiari Province, Iran. Arabian Journal of Geosciences, 9(3), 201.  https://doi.org/10.1007/s12517-015-2258-9.CrossRefGoogle Scholar
  81. Shafer, G. (1976). A mathematical theory of evidence. Princeton University Press. https://press.princeton.edu/titles/2439.html. Accessed 5 April 2018.
  82. Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T., & Zeileis, A. (2008). Conditional variable importance for random forests. BMC Bioinformatics, 9(1), 307.  https://doi.org/10.1186/1471-2105-9-307.CrossRefGoogle Scholar
  83. Tahmassebipoor, N., Rahmati, O., Noormohamadi, F., & Lee, S. (2016). Spatial analysis of groundwater potential using weights-of-evidence and evidential belief function models and remote sensing. Arabian Journal of Geosciences, 9(1), 79.  https://doi.org/10.1007/s12517-015-2166-z.CrossRefGoogle Scholar
  84. Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2013). Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. Journal of Hydrology, 504, 69–79.  https://doi.org/10.1016/j.jhydrol.2013.09.034.CrossRefGoogle Scholar
  85. Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2014). Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. Journal of Hydrology, 512, 332–343.  https://doi.org/10.1016/j.jhydrol.2014.03.008.CrossRefGoogle Scholar
  86. Tien Bui, D., Pradhan, B., Lofman, O., Revhaug, I., & Dick, O. B. (2012). Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena, 96, 28–40.  https://doi.org/10.1016/j.catena.2012.04.001.CrossRefGoogle Scholar
  87. Tien Bui, D., Tuan, T. A., Klempe, H., Pradhan, B., & Revhaug, I. (2016). 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(2), 361–378.  https://doi.org/10.1007/s10346-015-0557-6.CrossRefGoogle Scholar
  88. Umar, Z., Pradhan, B., Ahmad, A., Jebur, M. N., & Tehrany, M. S. (2014). Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. Catena, 118(September 2009), 124–135.  https://doi.org/10.1016/j.catena.2014.02.005.CrossRefGoogle Scholar
  89. Vorpahl, P., Elsenbeer, H., Märker, M., & Schröder, B. (2012). How can statistical models help to determine driving factors of landslides? Ecological Modelling, 239, 27–39.  https://doi.org/10.1016/j.ecolmodel.2011.12.007.CrossRefGoogle Scholar
  90. Wan, S., Lei, T.-C., & Chou, T.-Y. (2012). A landslide expert system: image classification through integration of data mining approaches for multi-category analysis. International Journal of Geographical Information Science, 26(4), 747–770.  https://doi.org/10.1080/13658816.2011.613397.CrossRefGoogle Scholar
  91. Xia, J., Falco, N., Benediktsson, J. A., Du, P., & Chanussot, J. (2017). Hyperspectral image classification with rotation random forest via KPCA. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(4), 1601–1609.  https://doi.org/10.1109/JSTARS.2016.2636877.CrossRefGoogle Scholar
  92. Youssef, A. M., Pradhan, B., Jebur, M. N., & El-Harbi, H. M. (2015). Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia. Environmental Earth Sciences, 73(7), 3745–3761.  https://doi.org/10.1007/s12665-014-3661-3.CrossRefGoogle Scholar
  93. Zabihi, M., Pourghasemi, H. R., Pourtaghi, Z. S., & Behzadfar, M. (2016). GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran. Environmental Earth Sciences, 75(8), 665.  https://doi.org/10.1007/s12665-016-5424-9.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Seyed Amir Naghibi
    • 1
    Email author
  • Mojtaba Dolatkordestani
    • 2
  • Ashkan Rezaei
    • 3
  • Payam Amouzegari
    • 1
  • Mostafa Taheri Heravi
    • 4
  • Bahareh Kalantar
    • 5
  • Biswajeet Pradhan
    • 6
    • 7
  1. 1.Department of Watershed Management Engineering, Faculty of Natural ResourcesTarbiat Modares University (TMU)NoorIran
  2. 2.Jiroft University Scholarship, Department of Combat Desertification, College of Natural ResourcesJiroft UniversityJiroftIran
  3. 3.Department of Range and Watershed Management, Faculty of Agriculture and Natural Resources SciencesUniversity of HormozganBandar AbbasIran
  4. 4.Department of Civil EngineeringEghbal Lahoori Institute of Higher EducationMashhadIran
  5. 5.RIKEN Center for Advanced Intelligence Project, Goal-Oriented Technology Research GroupDisaster Resilience Science TeamTokyoJapan
  6. 6.The Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and ITUniversity of Technology SydneySydneyAustralia
  7. 7.Department of Energy and Mineral Resources EngineeringChoongmu-gwan, Sejong UniversitySeoulKorea

Personalised recommendations