- Abdi, P. (2006). Investigation of flood potential of Zanjan River basin by SCS method and GIS. National Irrigation and Drainage Committee. Technical workshop on coexistence with floods. (In Persion)
- Akgün, A., & Bulut, F. (2007). GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region. Environment Geology, 51(8), 1377-1387.
- Albers, S. J., Déry, S. J., & Petticrew, E. L. (2016). Flooding in the Nechako River Basin of Canada: A random forest modeling approach to flood analysis in a regulated reservoir system. Canadian Water Resources Journal/Revue canadienne des ressources hydriques, 41(1-2), 250-260.
- Angileri, S.E., Conoscenti, C., Hochschild, V., Märker, M., Rotigliano, E., & Agnesi, V. (2016). Water erosion susceptibility mapping by applying Stochastic Gradient Treeboost to the Imera Meridionale River basin (Sicily, Italy). Geomorphology. 262, 61-76.
- Bui, D.T., Khosravi, K., Shahabi, H., Daggupati, P., Adamowski, J.F., Melesse, A., Pham, B.T., Pourghasemi, H.R., Mahmoodi, M., Bahrami, S., Pradhan, B., Shirzadi, A., Chapi, K., & Lee, S. (2019). Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model. Remote Sensing, 11(13), 1589.
- Chen, W., Li, Y., Xue, W., Shahabi, H., Li, S., Hong, H., & Ahmad, B.B. (2020). Modeling flood susceptibility using data-driven approaches of naïve bayes tree, alternating decision tree, and random forest methods. Science of The Total Environment, 701, 134-979.
- Conoscenti, C., Angileri, S., Cappadonia, C., Rotigliano, E., Agnesi, V., & Märker, M. (2014). Gully erosion susceptibility assessment by means of GIS-based logistic regression: a case of Sicily (Italy). Geomorphology, 204, 399-411.
- Dickie, J.A., & Parsons, A.J. (2012). Eco‐geomorphological processes within grasslands, shrublands and badlands in the semi‐arid Karoo, South Africa. Land Degradation Dev., 23(6), 534-547.
- Daoud, J.I. (2017). Multicollinearity and regression analysis. J. Phy, Conference Series (949(1), 012009). IOP Publishing.
- Felicĺsimo, Á., Cuartero, A., Remondo, J., & Quirόs, E. (2013). Mapping landslide susceptibility with logistiv regression, multiple adaptive regression splines, classification and regression tress, and maximum entropy methods: a comparative study. Landslides, 10, 175-189.
- Gayen, A., Pourghasemi, H.R., Saha, S., Keesstra, S., & Bai, S. (2019). Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms. Science of the Total Environment, 668, 124-138.
- Guzzetti, F., Cardinali, M., Reichenbach, P., & Carrara, A. (2000). Comparing landslide maps: A case study in the upper Tiber River Basin, central Italy. Environmental Management, 25(3), 247-263.
- Glenn, E., Morino, K., Nagler, P., Murray, R., Pearlstein, S., & Hultine, K. (2012). Roles of saltcedar (Tamarix spp.) and capillary rise in salinizing a non-flooding terrace on a flow-regulated desert river. Journal of Arid Environment, 79, 56-65.
- Hall, A. J. (1981). Flash flood forecasting. World Meteorological Organization (WMO (Series); no. 577.), Operational hydrology report (World Meteorological Organization); 18, 48.
- Hosmer, D. W., & Lemeshow, S. (2000). Multiple Logistic Regression. Hoboken, NJ: John Wiley & Sons, Inc. doi: 10.1002/0471722146.ch2.
- Jafarian, Z., & Kargar, M. (2017). Comparison of Random Forest (RF) and Boosting Regression Tree (BRT) For Prediction of Dominant Plant Species Presence in Polour Rangelands, Mazandaran Province. Iranian Journal of Applied Ecology, 6(1), 41-55.
- Kheyrizadeh, M., J. Maleki and H. Amounia. 2012. Flood hazard zoning using ANP model in watershed, case study: Mardaghchay Watershed. Quantitative Geomorphological Researches, 3(2), 39-56. (in Persian)
- Khosravi, K., Nohani, E., Maroufinia, E., & Pourghasemi, H.R. (2016). A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique. Natural Hazards, 83(2), 947-987.
- Lee, S., & Pradhan, B. (2007). Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides, 4(1), 33-41.
- Marmion, M., Hjort, J., Thuiller, W., & Luoto, M. (2008). A comparison of predictive methods in modelling the distribution of periglacial landforms in Finnish Lapland. Earth Surface Processes and Landforms, 33(14), 2241-2254,
- Mojaddadi, H., Pradhan, B., Nampak, H., Ahmad, N., & Ghazali, A.H.B. (2017). Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS. Geomatics, Natural Hazards and Risk, 8(2), 1080-1102.
- Nouri Boroujerdi, P., & Eskandi, V. (2009) Introduction to Quantitative Studies in Management (Case Study: Data Mining in Management Studies). Quarterly Journal of Quantitative Studies in Management, 3(2) 1-13 (In Persion)
- Poudyal, C.P., Chang, C., Oh, H.J., & lee, S. (2010). Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya. Environmental Earth Sciences, 61(5), 1049-1064.
- Pourghasemi, H.R., Jirandeh, A.G., Pradhan, B., Xu, C., & Gokceoglu, C. (2013). Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. Journal of Earth System Science, 122(2), 349-369.
- 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(3), 643-662
- Rahi, G.h. (2018). Prediction of trench erosion sensitivity using spatial data mining methods. Ph.D. thesis, Faculty of Natural Resources Engineering. Sari University of Agricultural, Sciences and Natural Resources. (In Persion).
- Rahmati, O., Pourghasemi, H. R., & Zeinivand, H. (2015). Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran. Geocarto International, 31(1), 42-70
- Rahmati, O., Zeinivand, H., & Besharat, M. (2016a). Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis. Geomatics, Natural Hazards and Risk, 7(3), 1000-1017.
- Rahmati, O., Pourghasemi, H. R., & Melesse, A. M. (2016b). 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, 360-372.
- Rahmati, O., & Pourghasemi, H. R. (2017). Identification of critical flood prone areas in data-scarce and ungauged regions: A comparison of three data mining models. Water Resources Management, 31(5), 1473-1487
- Rotigliano, E., Martinello, C., Agnesi, V., & Conoscenti, C. (2018). Evaluation of debris flow susceptibility in El Salvador (CA): a comparison between Multivariate Adaptive Regression Splines (MARS) and Binary Logistic Regression (BLR). Hungarian Geogr. Bull, 67, 361-373.
- Servati, M.R., Ghahrodi Tali, M., Golkarami, A., & Njafi, E. (2014). Geomorphological thresholds for gully erosion in Kchick watershed, NE Golestan Province. Applied researches in geographical sciences, 32, 231-249, (in Persian)
- 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.
- Tehrany, M.S., 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.
- Vapnik, V. (1995). The Nature of Statistical Learning Theory. New York, Springer-Verlag, pp. 122.
- Wilson, J.P., & Gallant, J.C. (Eds). (2000). Terrain analysis: principles and applications. John Wiley and Sons.
- Walter, S.D. (2002). Properties of the summary receiver operating characteristic (SROC) curve for diagnostic test data. Stat Med. 21, 1237-1256.
- Wang, L. (2005). Support Vector Machines: Theory and Applications. New York, Springer-Verlag, pp.412.
- Woznicki, S.A., Baynes, J., Panlasigui, S., Mehaffey, M., & Neale, A. (2019). Development of a spatially complete floodplain map of the conterminous United States using random forest. Science of the total environment, 647, 942-953.
- Yalcin, A. (2008). GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. Catena, 72)1), 1-12.
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