بررسی تأثیر سناریوهای عدم قطعیت در مدلسازی چشمه‌های آب زیرزمینی با استفاده از مدل مکسنت در حوزه‌ی آبخیز نمرود، تهران

Arkoprovo, B., Adarsa, J., Shashi Prakash, S. (2012). Delineation of groundwater potential zones using satellite remote sensing and geographic information techniques: a case study from Ganjam district, Orissa, India. Research Journal of Recent Sciences, 9, 59–66.

Austin, M. (2007). Species distribution models and ecological theory, a critical assessment and some possible new approaches. Ecological Modelling, 200(1),1–19

Ariyanto, A.C. (2015). Mapping of possible corridors for Javan Leopard (Panthera pardus ssp. melas) between Gunung Merapi and Gunung Merbabu National Parks, Indonesia. Doctoral dissertation, University of Twente.

Azareh, A., Rahmati, O., Rafiei-Sardooi, E., Sankey, J.B., Lee, S., Shahabi, H., and Ahmad, B.B. (2019). Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models. Science of the Total Environment, 655, 684-696.

Baldwin, R.A. (2009). Use of maximum entropy modeling in wildlife research. Entropy 11(4), 854–866

Beven, K., and Freer, J. (2001). A dynamic TOPMODEL. Hydrological Process, 15(10), 1993–2011.

Böhner, J., and Selige, T. (2006). Spatial prediction of soil attributes using terrain analysis and climate regionalisation. Gottinger Geographische Abhandlungen, 115, 13-28.

Bhattacharya, A.K. (2010). Artificial ground water recharge with a special reference to India. International Journal of Research and Reviews in Applied 4, 214–221.

Bera, K., and Bandyopadhyay, J. (2012). Ground water potential mapping in Dulung watershaed using remote sensing and GIS techniques, West Bangal, India. International Journal of Scientific and Research Publication, 2(12), 1–7.

Conforti, M., Pascale, S., Robustelli, G., and Sdao, F. (2014). Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turblo River catchment (northern Calabria, Italy). Catena, 113, 236-250.

Dehnavi, A., Aghdam, I.N., Pradhan, B., and Varzandeh, M.H.M. (2015). A new hybride model using step- wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. Catena, 135, 122-148.

Demir, G., Aytekin, M., and Akgun, A. (2015). Landslide susceptibility mapping by frequency ratio and logistic regression methods: an example from Niksar–Resadiye (Tokat, Turkey). Arabian Journal of Geosciences, 8(3), 1801-1812.

Daoud, J.I. (2017). Multicollinearity and regression analysis. J. Phy, Conference Series. 949(1), 012009). IOP Publishing.

Davoodi Moghaddam, D., Rezaei, M., Pourghasemi, H.R., Pourtaghie, Z.S., and Pradhan, B. (2013). Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan watershed, Iran. Arabian Jornal of Geoscience, 8(2), 913–929.

Devkota, K.C., Regmi, A.D., Pourghasemi, H.R., Yoshida, K., Pradhan, B., and Ryu, I.C. (2013). Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya. Natural Hazards, 65(1), 135–165.

Deepa, S., Venkateswaran, S., Ayyandurai, R. (2016). Groundwater recharge potential zones mapping in upper Manimuktha Sub basin Vellar river Tamil Nadu India using GIS and remote sensing techniques. Model Earth Syst Environ 2(137). https ://doi. org/10.1007/s4080 8-016-0192.

Felicĺsimo, Á., Cuartero, A., Remondo, J., and Quirόs, E. (2013). Mapping landslide susceptibility with logistiv regression, multiple adaptive regression splines, classification and regression tress, amd maximum entropy methods: a comparative study. Landslides, 10, 175-189

Jungerius, P.D., Matundura, J., and Van de Ancker, J.A.M. (2002). Road construction and gully erosion in West Pokot, Kenya. Earth Surf Process Landf, 27, 1237–1247.

Jaiswal RK, Mukherjee S, Krishnamurthy J, Saxena R (2003) Role of remote sensing and GIS techniques for generation of groundwater prospect zones towards rural development—an approach. Int J Remote Sens 24(5):993–1008.

Israil M, Al-hadithi M, Singhal DC, Kumar B, Rao MS, Verma K (2006) Groundwater resources evaluation in the Piedmont zone of Himalaya, India, using isotope and GIS technique. J Spat Hydrol 6(1):34–38

Greene, W.H. (2000). Econometric Analysis (4th Edition), Upper Saddle River, NJ: Prentice Hall.

Godebo TR (2005) Application of remote sensing and GIS for geological investigation and groundwater potential zone identification, Southeastern Ethiopian Plateau, Bale Mountains and the surrounding areas. ADDIS ABABA UNIVERSITY, Dissertation

Gomez, H., and Kavzoglu, T. (2005). Assessment of shallow landslide susceptibility using artificial neural networks in jabonosa River Basin, Venezuela. Engineering Geology, 78(1), 11-27.

Gutie´rrez, A.G., Schnabel, S., Contador, J.F.L. (2009). Using and comparing two nonparametric methods (CART and MARS) to model the potential distribution of gullies. Ecological Modelling, 220, 3630–3637.

Golkarian, A., and Rahmati, O. (2018). Use of a maximum entropy model to identify the key factors that influence groundwater availability on the Gonabad Plain, Iran. Environmental earth sciences,77(10), 1-20.

Ghosh, S. and Carranza, E.J.M. (2010). Spatial analysis of mutual fault/fracture and slope controls on rock sliding in Darjeeling Himalaya, India. Geomorphology 122, 1- 24.

Gallardo-Cruz, J.A., Pérez-García, E.A., and Meave, J.A. (2009). β-Diversity and vegetation structure as influenced by slope aspect and altitude in a seasonally dry tropical landscape. Landsc Ecol, 24, 473–482.

Geroy, I.J., Gribb, M.M., Marshall, H.P., Chandler, D.G., Benner, S.G., and McNamara, J.P. (2011). Aspect influences on soil water retention and storage. Hydrol Processes, 25, 3836–3842.

Javidan, N., Kavian, A., Pourghasemi, H.R., Conoscenti, C., and Jafarian, Z. (2020). Gully Erosion Susceptibility Mapping Using Multivariate Adaptive Regression Splines—Replications and Sample Size Scenarios. Water, 11(11), 231-239.

Jaime, R., Alcjntara, J.M., Bastida, J.M., Rey, P.J. (2015). Complex patterns of environmental niche evolution in Iberian columbines (genus Aquilegia, Ranunculaceae). Journal of Plant Ecology, 8(5), 457–467

Kornejady, A., Ownegh, M., and Bahremand, A. (2017). Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. Catena, 152, 144-162.

Lee, S., and Pradhan, B. (2007). Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides, 4, 33-41.

Lee, S., Song, K.Y., and Kim, Y. (2012). Regional groundwater productivity potential mapping using a geographic information system (GIS) based artificial neural network model. Hydrogeol J 20, 1511–1527.

Moghaddam, D.D., Rezaei, M., Pourghasemi, H.R., Pourtaghie, Z.S., and Pradhan, B. (2015). Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan watershed, Iran. Arabian Journal of Geosciences, 8(2), 913-929.

Manap, M.A., Nampak, H., Pradhan, B., Lee, S., Soleiman Wan Nor, A., and Ramli, M. F. (2012), Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS, Arabian Journal of Geosciences, 7, 711-724.

Mancini, F., Ceppi, C., and Ritrovato, G. (2010). GIS and statistical analysis for landslide susceptibility mapping in the Daunia area, Italy. Natural Hazards and Erth System Sciences, 10(9): 1851-1859.

Moore, I.D., Grayson, R.B., and Ladson, A.R. (1991). Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrology Process, 5, 3-30.

Meinhardt, M., Fink, M., and Tunschel, H. (2015). Landslide susceptibility analysis in central Vietnam based on an incomplete landslide inventory: comparison of a new method to calculate weighting factors by means of bivariate statistics. Geomorphology, 234, 80–97.

Madrucci V, Taioli F, Cesar de Araujo C (2008) Groundwater favorability map using GIS multicriteria data analysis on crystalline terrain, Sao Paulo State, Brazil. J Hydrol 357:153–173

Naghibi, S. A., Pourghasemi, H. R., Pourtaghi, Z. S., and 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), 171-186.

Nguyen, P. T., Ha, D.H., Avand, M., Jaafari, A., Nguyen, H.D., Al-Ansari, N., and Ho, L.S. (2020), Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping. Applied Sciences, 10(7), 2469-2478.

Naghibi, S.A., Pourghasemi, H.R., Dixon, B. (2016) Groundwater spring potential using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environ Monit Assess. doi:10.1007/s10661-015-5049-6

Oh, H.J., Kim, Y.S., Choi, J.K., Park, E., and 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.

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, 290-308.

Pearce, J., Ferrier, S. (2000). Evaluating the predictive performance of habitat models developed using logistic regression. Ecol Model, 133(3), 225–245

Park, N.W. (2015). Using maximum entropy modeling for landslide susceptibility mapping with multiple geoenvironmental data sets. Environmental Earth Sciences, 73(3), 937-949.

Pourtaghi, Z.S., and 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.

Pradhan, B. (2009). Groundwater potential zonation for basaltic watersheds using satellite remote sensing data and GIS techniques, Central European Journal of Geosciences, 1(1), 120–129.

Pourghasemi, H.R., and Rossi, M. (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. Theor Appl Climatol 130(1–2), 609–633.

Pourghasemi, H.R., Kornejady, A., Kerle, N., and Shabani, F. (2020). Investigating the effects of different landslide positioning techniques, landslide partitioning approaches, and presence-absence balances on landslide susceptibility mapping. Catena, 187, 104364-76.

Riley, S.J., Degioria, S.D., and Elliot, R. (1999). Index that quantifies topographic heterogeneity. intermountain Journal of sciences, 5(1-4), 23-27.

Rahmati, O., Pourghasemi, H.R., and 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, 360-372.

Rahmati, O., Tahmasebipour, N., Haghizadeh, A., Pourghasemi, H.R., and Feizizadeh, B. (2017). Evaluating the influence of geo-environmental factors on gully erosion in a semi-arid region of Iran: An integrated framework. Science of The Total Environment, 579, 913-927.

seyed ali, S., rahimi, M., Dastourani, J., and Khosroshahi, M. (2016). Trend Analysis of Hydroclimatological Parameters and Detection of Manageral Changes in Water Resources Conditions of Hablerood Watershed, Iranian Journal of Range and Desert Research, 23(3), 555-566.

Shahin, K.A., and Hassan, N. (2000). Sources of shared variability among body shape characters at marketing age in New Zealand White and Egyptian rabbit breeds. Annales de Zootechnie, 49, 435- 445.

Siahkamari, S., Haghizadeh, A., Zeinivand, H., Tahmasebipour, N., and Rahmati, O. (2018). Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models. Geocarto international, 33(9), 927-941.

Sidel, R.C., and Ochiai, H. (2006). Landslides: Processes, Prediction, and Land use, Water Resource Monograph: 18, AGU books, ISSN: 0170-9600.

Shirzadi, A., Saro, L., Hyun-Joo, O.h., and Chapi, K. (2012). A GIS-based logistic regression model in rock fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran. Natural Hazard. 64, 1639-1656.

Shirzadi, A., Soliamani, K., Habibnejhad, M., Kavian, A., Chapi, K., Shahabi, H., ... and Tien Bui, D. (2018). Novel GIS based machine learning algorithms for shallow landslide susceptibility mapping. Sensors, 18(11), 3777.

Shary, P.A., Sharaya, L.S., and Mitusov, A.V. (2002). Fundamental quantitative methods of land surface analysis. Geoderma, 107 (1), 1–32.

Shannon, C.E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379–423, 623–656 [Mathematical Reviews. (MathSciNet), MR10, 133e]

Thuiller W, Richardson DM, Pyšek P, Midgley GF, Hughes GO, Rouget M (2005) Niche-based modeling as a tool for predicting

the risk of alien plant invasions at a global scale. Glob Chang Biol 11(12):2234–2250

-Tehrany M.S., Pradhan B., Mansor S. and Ahmad N. (2015). Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena, 125, 91-101.

Vandekerckhove, L., Poesen, J., and Govers, G. (2003). Medium-term gully headcut retreat rates in Southeast Spain determined from aerial photographs and ground measurements. Catena, 50,  329-352.

Vorpahl, P., Elsenbeer, H., Mӓrker, M., and Schröder, B. (2012). How can statistical models help to determine driving factors of landslides? Ecological Modelling, 239, 27-39.

Williams RJ (2010) Simple MaxEnt models explain food web degree distributions. Theor Ecol 3(1), 45–52.

Walter, S.D. (2002). Properties of the summary receiver operating characteristic (SROC) curve for diagnostic test data. Stat Med. 21, 1237–1256.

Xu, C., Dai, F., Xu, X., and Lee, Y.H. (2012). GIS-based support vector mechine modeling of earthquake-triggered landslide susceptibility in the jianjiand River watershed, China. Geomorphology, 145, 70-80.

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, 1–12.

Yessilnacar, E.K. (2005). The application of computational intelligence of landslide susceptibility mapping in Turkey. Ph. D Thesis Department of Geomatics the University of Melbourne. 423 pages.

Zabihi, M., Pourghasemi, H.R., Pourtaghi, Z. S., and Behzadfar, M. (2016). GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran. Environmental Earth Sciences, 75(8), 665-674.