1. Al-Abadi, A.M., B. Pradhan and S. Shahid. 2016. Prediction of groundwater flowing well zone at An-Najif Province, central Iraq using evidential belief functions model and GIS. Environmental monitoring and assessment, 188(10): 549. [
DOI:10.1007/s10661-016-5564-0]
2. Althuwaynee, O.F., B. Pradhan, H.J. Park and J.H. Lee. 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. [
DOI:10.1016/j.catena.2013.10.011]
3. Atkinson, P.M., H. Jiskoot, R. Massari and T. Murray. 1998. Generalized linear modelling in geomorphology. Earth Surf. Proc. Land, 23: 1185-1195.
https://doi.org/10.1002/(SICI)1096-9837(199812)23:13<1185::AID-ESP928>3.0.CO;2-W [
DOI:10.1002/(SICI)1096-9837(199812)23:133.0.CO;2-W]
4. Bayrami, H., M. Neishabouri, A. Nazemi and F. Abbasi. 2015. The Effect of Soil Reinforcement on Penetration Characteristics in Clay Loam and Sandy Loam. Journal of Water and Soil Science, 25 (2): 181- 192 (In Persian).
5. Beguería, S. 2006. Validation and Evaluation of Predicitve Models in Hazard Assessment and Risk Management. Natural Hazards and Earth System Science, 37: 315-329. [
DOI:10.1007/s11069-005-5182-6]
6. Behyari, M., A. Alizadeh and Sh. Mahmoudian. 2017. Evaluation of the effect of active structures in land subsidence hazard using multivariate decision making models. Advanced Applied Geology Journal, 24: 49- 56 (In Persian).
7. Brenning, A. 2005. Spatial prediction models for landslide hazards: review, comparison and evaluation, Natural Hazards and Earth System Science, 5: 853-862. [
DOI:10.5194/nhess-5-853-2005]
8. Chen, W., B., Pradhan, S. Li, H. Shahabi, H.M. Rizeei, E. Hou and S. Wang. 2019. Novel Hybrid Integration Approach of Bagging-Based Fisher's Linear Discriminant Function for Groundwater Potential Analysis. Natural Resources Research, 1-20. [
DOI:10.1007/s11053-019-09465-w]
9. Choi, W., U. Galasinski, S.J. Cho and C.S. Hwang. 2012. A spatiotemporal analysis of groundwater level changes in relation to urban growth and groundwater recharge potential for Waukesha County. Wisconsin. Geographical Analysis, 44(3): 219-234. [
DOI:10.1111/j.1538-4632.2012.00848.x]
10. Chowdhury, A., M.K. Jha and V.M. Chowdary. 2011. Delineation of groundwater recharge zones and identification of artificial recharge sites in West Medinipur District, West Bengal using RS, GIS and MCDM techniques. Environ Earth Sci, 59(6): 1209-1222. [
DOI:10.1007/s12665-009-0110-9]
11. Comprehensive Studies of Azna and Aligodarz Watershed, Lorestan Province. (2012). Regional Water Company of Lorestan Province (RWCL) vol: 3& 16.
12. Corsini, A., F. Cervi and F. Ronchetti. 2009. Weight of evidence and artificial neural networks for potential groundwater spring mapping: an application to the Mt. Modino area (Northern Apennines, Italy). Geomorphology, 111: 79-87. [
DOI:10.1016/j.geomorph.2008.03.015]
13. Dominic, A.R. and F. Zimmermann. 2010. Modelling potential Snow Leopard (Uncia uncia) habitat in the trans-Himalayan ranges using MaxEnt with emphasis on the evaluation techniques.
14. Dowell, S.A. and E.R. Hekkala. 2016. Divergent lineages and conserved niches: using ecological niche modeling to examine the evolutionary patterns of the Nile monitor (Varanus niloticus). Evolutionary ecology, 30(3): 471-485. [
DOI:10.1007/s10682-016-9818-7]
15. Elith, J., S.J. Phillips, T. Hastie, M. Dudík, Y.E. Chee and C.J. Yates. 2011. A statistical explanation of MaxEnt for ecologists. Diversity and distributions, 17(1): 43-57. [
DOI:10.1111/j.1472-4642.2010.00725.x]
16. Exploratory studies on soil conservation and watershed management of Marboreh watershed and a small part of the Tireh river in northern Dorood city, Lorestan and Markazi provinces. (2002). Forest, Rangeland and Watershed Management of Iran (FRWMI), Soil and land classification, Vol: 4 & 6.
17. Fagbohun, B.J. 2018. Integrating GIS and multi-influencing factor technique for delineation of potential groundwater recharge zones in parts of Ilesha schist belt, southwestern Nigeria. Environmental Earth Sciences, 77(3): 69. [
DOI:10.1007/s12665-018-7229-5]
18. Guns, M. and V. Vanacker. 2012. Logistic regression applied to natural hazards: rare event logistic regression with replications. Natural Hazards and Earth System Science, 12: 1937-1947. [
DOI:10.5194/nhess-12-1937-2012]
19. Guo, C., D.R. Montgomery, Y. Zhang, K. Wang and Z. Yang. 2015. Quantitative assessment of landslide susceptibility along the Xianshuihe fault zone, Tibetan Plateau, China. Geomorph, 248: 93-110. [
DOI:10.1016/j.geomorph.2015.07.012]
20. Guzzetti, F., P. Reichenbach, F. Ardizzone, M. Cardinali and M. Galli. 2006. Estimating the quality of landslide susceptibility models, Geomorphology, 81: 166-184. [
DOI:10.1016/j.geomorph.2006.04.007]
21. Heckmann, T., K. Gegg, A. Gegg and M. Becht. 2014. Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows. Natural Hazards and Earth System Science, 14: 259-278. [
DOI:10.5194/nhess-14-259-2014]
22. Herrera, C., C. Gamboa, E. Custodio, T. Jordan, L. Godfrey, J. Jódar, J.A. Luque, J. Vargas and A. Sáez. 2018. Groundwater origin and recharge in the hyperarid Cordillera de la Costa, Atacama Desert, northern Chile. Science of the Total Environment, 624: 114-132. [
DOI:10.1016/j.scitotenv.2017.12.134]
23. Hjort, J. and M. Marmion. 2008. Effects of sample size on the accuracy of geomorphological models. Geomorphology, 102: 341-350. [
DOI:10.1016/j.geomorph.2008.04.006]
24. Huang, C.C., H.F. Yeh, H.I. Lin, S.T. Lee, K.C. Hsu and C.H. Lee. 2013. Groundwater recharge and exploitative potential zone mapping using GIS and GOD techniques. Environmental earth sciences, 68(1): 267-280. [
DOI:10.1007/s12665-012-1737-5]
25. Krstanovic, P.F and V.P. Singh. 1993. A real-time flood forecasting model based on maximum-entropy spectral analysis: I. Development. Water resources management, 7(2): 109-129. [
DOI:10.1007/BF00872477]
26. Kumar, A. and A.P. Krishna. 2018. Assessment of groundwater potential zones in coal mining impacted hard-rock terrain of India by integrating geospatial and analytic hierarchy process (AHP) approach. Geocarto International, 33(2): 105-129. [
DOI:10.1080/10106049.2016.1232314]
27. Lee, S. and K. Min. 2001. Statistical analysis of landslide susceptibility at Youngin, Korea. Environ Geol, 40: 1095-1113. [
DOI:10.1007/s002540100310]
28. Legendre, P. 1993. Spatial Autocorrelation: Trouble or New Paradigm? Ecology, 74: 1659-1673. [
DOI:10.2307/1939924]
29. Lombardo, L. and P.M. Mai. 2018. Presenting logistic regression-based landslide susceptibility results. Engineering geology, 244: 14-24. [
DOI:10.1016/j.enggeo.2018.07.019]
30. Luoto, M. and J. Hjort. 2005. Evaluation of current statistical approaches for predictive geomorphological mapping. Geomorphology, 67: 299-315. [
DOI:10.1016/j.geomorph.2004.10.006]
31. Machiwal, D. and P.K. Singh. 2015. Comparing GIS-based multi-criteria decision-making and Boolean logic modelling approaches for delineating groundwater recharge zones. Arabian Journal of Geosciences, 8(12): 10675-10691. [
DOI:10.1007/s12517-015-2002-5]
32. Mahmudi Aznaveh, B. 2017. Evaluation of methods for categorizing the statistical diagnostic model - the twelfth section (01-711-10-41). Shahid Beheshti University, Research Institute of Cyberspace, 1-19 (In Persian).
33. Manap, M.A., H. Nampak, B. Pradhan, S. Lee, W.N.A. Sulaiman and M.F. Ramli. 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. [
DOI:10.1007/s12517-012-0795-z]
34. Meusburger, K. and C. Alewell. 2009. on the influence of temporal change on the validity of landslide susceptibility maps. Natural Hazards and Earth System Science, 9: 1495-1507. [
DOI:10.5194/nhess-9-1495-2009]
35. Montgomery, D.R. and W.E. Dietrich. 1994. A physically based model for the topographic control on shallow land sliding. Water Resources Research, 30: 1153-1171. [
DOI:10.1029/93WR02979]
36. Mousavi, S.M., A. Golkarian, S.A. Naghibi, B. Kalantar and B. Pradhan. 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. [
DOI:10.3934/geosci.2017.1.91]
37. Naghibi, S.A. and M.M. Dashtpagerdi. 2017. Evaluation of four supervised learning methods for groundwater spring potential mapping in Khalkhal region (Iran) using GIS-based features. Hydrogeology journal, 25(1): 169-189. [
DOI:10.1007/s10040-016-1466-z]
38. Naghibi, S.A., H.R. Pourghasemi and B. Dixon. 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. [
DOI:10.1007/s10661-015-5049-6]
39. Naghibi, S.A., H.R, Pourghasemi, Z.S. Pourtaghi and A. Rezaei. 2015. Groundwater qanat potential mapping using frequency ratio and Shannon's entropy models in the Moghan watershed, Iran. Earth Science Informatics, 8(1):171-186. [
DOI:10.1007/s12145-014-0145-7]
40. Oh, H.J., Y.S. Kim, J.K. Choi, E. Park and S. Lee. 2011. GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea. Journal of Hydrology, 399(3-4): 158-172. [
DOI:10.1016/j.jhydrol.2010.12.027]
41. Ohlmacher, G.C. and J.C. Davis. 2003. Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Engineering geology, 69: 331-343. [
DOI:10.1016/S0013-7952(03)00069-3]
42. Oliyayi, A., N. Parvian and O. Khsravi. 2017. Identifying the potential of groundwater resources in hard formations as a way to manage water crisis, Case study: Kalat Naderi watershed. Geography and environmental hazards, 24: 143-158.
43. Ozdemir, A. 2011. 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. [
DOI:10.1016/j.jhydrol.2011.10.010]
44. Petschko, H., A. Brenning, R. Bell, J. Goetz and T. Glade. 2014. Assessing the quality of landslide susceptibility maps - case study Lower Austria. Natural Hazards and Earth System Science. Sci, 14: 95-118. [
DOI:10.5194/nhess-14-95-2014]
45. Phillips, S.J., M. Dudík, J. Elith, C.H. Graham, A. Lehmann, J. Leathwick and S. Ferrier. 2009. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological applications. 19(1): 181-197. [
DOI:10.1890/07-2153.1]
46. Piroozinejad, S., K. Soleimani, M. Habibnejad and R. Zakerinejad. 2017. Gully erosion predictions using Alos radar and Maxent model in Alvand basin. Remote Sensing and GIS Iran, 9(4): 95-110 (In Persian).
47. Pourghasemi H.R., M. Mohammady and B. Pradhan. 2012. Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin. Iran. Catena, 97: 71-84. [
DOI:10.1016/j.catena.2012.05.005]
48. Pourghasemi, H.R and M. Rossi. 2017. Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods. Theoretical and Applied Climatology, 130(1-2): 609-633. [
DOI:10.1007/s00704-016-1919-2]
49. Pourghasemi, H.R., S. Yousefi, A. Kornejady and A. Cerdà. 2017. Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. Science of the Total Environment, 609: 764-775. [
DOI:10.1016/j.scitotenv.2017.07.198]
50. Pourkazemi, A., H. Alizadeh Nouqabi and S. Republic. 2010. Estimation of entropy with Bootstrap and Jackknife methods and its application in the normal test. Journal of Statistical Sciences, 1- 26 (In Persian).
51. Pourtaghi, Z.S. and H.R. Pourghasemi. 2014. GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province, Iran. Hydrogeology Journal, 22(3): 643-662. [
DOI:10.1007/s10040-013-1089-6]
52. Prasad, R.K., N.C. Mondal, P. Banerjee, M.V. Nandakumar and V.S. Singh. 2008. Deciphering potential groundwater zone in hard rock through the application of GIS. Environ. Geol, 55(3): 467-475. [
DOI:10.1007/s00254-007-0992-3]
53. Quinn, S.A., J.P. Gibbs, M.H. Hall and P.J. Petokas. 2013. Multiscale factors influencing distribution of the eastern hellbender salamander (Cryptobranchus alleganiensis alleganiensis) in the northern segment of its range. Journal of Herpetol, 47 (1): 78-84. [
DOI:10.1670/11-127]
54. Ramalho, E.A. 2002. Regression models for choice-based samples with misclassification in the response variable. Journal of Econometrics, 106(1): 171-201. [
DOI:10.1016/S0304-4076(01)00094-X]
55. Razandi, Y., B. Farokhzadeh, M. Yousefzadeh Chabok, T. Timurian. 2017. Detection of groundwater resources potential using maximum entropy algorithm and geographic information system (Case study: Hamedan Bahar plain). Journal of Research in Irrigation and Water Engineering, 8(29): 110 - 123 (In Persian).
56. Ruette, J. v., A. Papritz, P. Lehmann, C. Rickli and D. Or. 2011. Spatial statistical modeling of shallow landslides -Validating predictions for different landslide inventories and rainfall events. Geomorphology, 133: 11-22. [
DOI:10.1016/j.geomorph.2011.06.010]
57. Rutherford, G.N., A. Guisan and N.E. Zimmermann. 2007. Evaluating sampling strategies and logistic regression methods for modelling complex land cover changes. Journal of Applied Ecology, 44(2): 414-424. [
DOI:10.1111/j.1365-2664.2007.01281.x]
58. Saha, A.K., R.P. Gupta, I. Sarkar, M.K. Arora and E. Csaplovics. 2005. An approach for GIS-based statistical landslide susceptibility zonation with a case study in the Himalayas. Landslides 2: 61-69. [
DOI:10.1007/s10346-004-0039-8]
59. Shafizadeh-Moghadam, H., R. Valavi, H. Shahabi, K. Chapi and A. Shirzadi. 2018. Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping. Journal of environmental management, 217: 1-11. [
DOI:10.1016/j.jenvman.2018.03.089]
60. Siahkamari, S., A. Haghizadeh, H. Zeinivand and N. Tahmasebipour. 2018. Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models. Geocarto international, 33(9): 927-941. [
DOI:10.1080/10106049.2017.1316780]
61. Siders, Z.A., A.J. Westgate, D.W. Johnston, L.D. Murison, and H.N. Koopman. 2013. Seasonal variation in the spatial distribution of basking sharks (Cetorhinus maximus) in the lower Bay of Fundy, Canada. PloS one, 8(12: e 82074. [
DOI:10.1371/journal.pone.0082074]
62. Stockwell, D. and A. Townsend Peterson. 2002. Effects of sample size on accuracy of species distribution models. Ecological modelling, 148: 1-13. [
DOI:10.1016/S0304-3800(01)00388-X]
63. Van Asselen, S. and A.C. Seijmonsbergen. 2006. Expert-driven semi-automated geomorphological mapping for a mountainous area using a laser DTM. Geomorphology, 78(3-4): 309-320. [
DOI:10.1016/j.geomorph.2006.01.037]
64. Van Den Eeckhaut, M., T. Vanwalleghem, J. Poesen, G. Govers, G. Verstraeten and L. Vandekerckhove. 2006. Prediction of landslide susceptibility using rare events logistic regression: A case-study in the Flemish Ardennes (Belgium). Geomorphology, 76: 392-410. [
DOI:10.1016/j.geomorph.2005.12.003]
65. www.Earthexplorerusgs.gov
66. Zabihi, M., H.R. Purghasemi and M. Behzadfar. 2015. Preparation of groundwater potential map using Shannon entropy models and random forest in Bojnourd plain. Ecohydrology, 2(2): 221-232.
67. Zeng, X., D. Wang and J. Wu. 2012. Sensitivity analysis of the probability distribution of groundwater level series based on information entropy. Stoch Env Res Risk A, 26: 345-356. [
DOI:10.1007/s00477-012-0556-2]