Skip to main content

Advertisement

Log in

Use of a maximum entropy model to identify the key factors that influence groundwater availability on the Gonabad Plain, Iran

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

The purpose of this study is to identify the key factors that influence the availability of groundwater resources in a 10-year period in the Gonabad region of Iran using a maximum entropy model. For this purpose, 165 qanats were selected in 2004 that had yields of more than 3 l/s. By reviewing similar studies, 13 factors were considered to have an influence on groundwater potential exploited by the qanats, including: slope aspect; drainage density; fault density; distance from faults or other fractures; land use; lithology; plan curvature; profile curvature; qanat density; distance from rivers; land slope; stream power index (SPI); and topographic wetness index (TWI). The results indicated that qanat density had the greatest influence on the groundwater potential in a given area. Additionally, the increased importance of this factor with the occurrence of drought indicates that qanats are appropriate structures for exploiting groundwater and that they were well sited when they were originally constructed. Other important factors that influence groundwater potential are SPI, TWI and lithology factors. This indicates the importance of the slope, water accumulation area and rock characteristics in the groundwater potential. Additionally, there were significant changes in the distribution of areas with a high to low groundwater potential over a 10-year period. Reducing the number of effective factors in 2014 modeling showed that with the occurrence of drought and the limitation of areas with groundwater potential, the factors affecting zoning of the groundwater potential are also limited.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Adiat KAN, Nawawi MNM, 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. J Hydrol 440:75–89. https://doi.org/10.1016/j.jhydrol.2012.03.028

    Article  Google Scholar 

  • Al Saud M (2010) Mapping potential areas for groundwater storage in Wadi Aurnah Basin, western Arabian Peninsula, using remote sensing and geographic information system techniques. Hydrogeol J 18:1481–1495. https://doi.org/10.1007/s10040-010-0598-9

    Article  Google Scholar 

  • Anbazhagan S, Ramasamy SM, Gupta DS (2005) Remote sensing and GIS for artificial recharge study, runoff estimation and planning in Ayyar basin, Tamil Nadu, India. Environ Geol 48:158–170

    Article  Google Scholar 

  • Ariyanto AC (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

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

    Article  Google Scholar 

  • Bajat B, Hengl T, Kilibarda M, Krunić N (2011) Mapping population change index in Southern Serbia (1961–2027) as a function of environmental factors. Comput Environ Urban Syst 35(1):35–44

    Article  Google Scholar 

  • Baldwin RA (2009) Use of maximum entropy modeling in wildlife research. Entropy 11(4):854–866

    Article  Google Scholar 

  • Bayumi T (2008) Quantitative groundwater resources evaluation in the lower part of Yalamlam basin, Makkah Al Mukarramah, Western Saudi Arabia. JKAU Earth Sci 19:35–56

    Article  Google Scholar 

  • Bera K, Bandyopadhyay J (2012) Ground water potential mapping in Dulung watershed using remote sensing and GIS techniques, West Bengal, India. Int J Sci Res Publ 2(12):1–7

    Google Scholar 

  • Berger AL, Pietra VJD, Pietra SAD (1996) A maximum entropy approach to natural language processing. Comput Linguist 22(1):39–71

    Google Scholar 

  • Bhattacharya AK (2010) Artificial ground water recharge with a special reference to India. Int J Res Rev Appl Sci 4:214–221

    Google Scholar 

  • Cardenas MB, Wilson JL, Zlotkik VA (2004) Impact of heterogeneity, bed forms, and stream curvature on subchannel hyporheic exchange. Water Resour Res 40(8):W083071–W0830713

    Article  Google Scholar 

  • Chowdhury A, Jha MK, Chowdary VM (2010) 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

    Article  Google Scholar 

  • Convertino M, Troccoli A, Catani F (2013) Detecting fingerprints of landslide drivers, a MaxEnt model. J Geophys Res Earth Surf 118(3):1367–1386

    Article  Google Scholar 

  • Dai FC, Lee CF, Li J, Xu ZW (2001) Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol 40(3):381–391

    Article  Google Scholar 

  • Davis J, Blesius L (2015) A hybrid physical and maximum-entropy landslide susceptibility model. Entropy 17(6):4271–4292

    Article  Google Scholar 

  • Davoodi Moghaddam D, Rezaei M, Pourghasemi HR, Pourtaghie ZS, Pradhan B (2013) Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan watershed, Iran. Arab J Geosci 8(2):913–929

    Article  Google Scholar 

  • Deepa S, Venkateswaran S, Ayyandurai R et al (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/s40808-016-0192-9

    Article  Google Scholar 

  • Dinesh Kumar PK, Gopinath G, Seralathan P (2007) Application of remote sensing and GIS for the demarcation of groundwater potential zones of a river basin in Kerala, southwest coast of India. Int J Remote Sens 28(24):5583–5601

    Article  Google Scholar 

  • Dyke J, Kleidon A (2010) The maximum entropy production principle, its theoretical foundations and applications to the earth system. Entropy 12(3):613–630

    Article  Google Scholar 

  • Edvardsen A, Bakkestuen V, Halvorsen R (2011) A fine-grained spatial prediction model for the red-listed vascular plant Scorzonera humilis. Nord J Bot 29(4):495–504

    Article  Google Scholar 

  • Edwards TC, Cutler DR, Zimmermann NE, Geiser L, Alegria J (2005) Model-based stratifications for enhancing the detection of rare ecological events. Ecology 86(5):1081–1090

    Article  Google Scholar 

  • Elith J, Phillips S, Hastie T, Dudík M, Chee Y, Yates C (2010) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17:43–47

    Article  Google Scholar 

  • Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17(1):43–57

    Article  Google Scholar 

  • Ettazarini S (2007) Groundwater potential index: a strategically conceived tool for water research in fractured aquifers. Environ Geol 52:477–487

    Article  Google Scholar 

  • Feeley KJ, Silman MR (2011) Keep collecting, accurate species distribution modeling requires more collections than previously thought. Divers Distrib 17(6):1132–1140

    Article  Google Scholar 

  • Geological Survey Department of Iran (GSDI) (1997). http://www.gsi.ir/Main/Lang_en/index.html. Accessed 20 May 2017

  • Ghorbani Nejad S, Falah F, Daneshfar M, Haghizadeh A, Rahmati O (2017) Delineation of groundwater potential zones using remote sensing and GIS-based data-driven models. Geocarto Int 32(2):167–187

    Google Scholar 

  • Gooseff MN, Anderson JK, Wondzell SM et al (2005) A modelling study of hyporheic exchange pattern and the sequence, size, and spacing of stream bedforms in mountain stream networks, Oregon, USA. Hydrol Proc 19(15):2915–2929. https://doi.org/10.1002/hyp.5790

    Article  Google Scholar 

  • Graham CH, Elith J, Hijmans RJ, Guisan A, Townsend Peterson A, Loiselle BA (2008) The influence of spatial errors in species occurrence data used in distribution models. J Appl Ecol 45(1):239–247

    Article  Google Scholar 

  • Guisan A, Broennimann O, Engler R, Vust M, Yoccoz NG, Lehmann A, Zimmermann NE (2006) Using niche-based models to improve the sampling of rare species. Conserv Biol 20(2):501–511

    Article  Google Scholar 

  • Gutiérrez AG, Schnabel S, Contador JFL (2009) Using and comparing two nonparametric methods (CART and MARS) to model the potential distribution of gullies. Ecol Model 220:3630–3637

    Article  Google Scholar 

  • Halvorsen R (2012) A gradient analytic perspective on distribution modelling. Sommerfeltia 35:1–165

    Article  Google Scholar 

  • Hernandez PA, Franke I, Herzog SK, Pacheco V, Paniagua L, Quintana HL et al (2008) Predicting species distributions in poorly-studied landscapes. Biodivers Conserv 17(6):1353–1366

    Article  Google Scholar 

  • Hortal J, Jiménez-Valverde A, Gómez JF, Lobo JM, Baselga A (2008) Historical bias in biodiversity inventories affects the observed environmental niche of the species. Oikos 117(6):847–858

    Article  Google Scholar 

  • Huset R (2013) A GIS-based analysis of the environmental predictors of dispersal of the emerald ash borer in New York. MA thesis, Syracuse University

  • Jaime R, Alcántara JM, Bastida JM, Rey PJ (2015) Complex patterns of environmental niche evolution in Iberian columbines (genus Aquilegia, Ranunculaceae). J Plant Ecol 8(5):457–467

    Article  Google Scholar 

  • Jaynes ET (1957a) Information theory and statistical mechanics. Phys Rev 106(4):620

    Article  Google Scholar 

  • Jaynes ET (1957b) Information theory and statistical mechanics. II. Phys Rev 108(2):171

    Article  Google Scholar 

  • Jenness J (2013) DEM surface tools for ArcGIS. Jenness Enterprises. http://www.jennessent.com/arcgis/surface:area.htm. Accessed Mar

  • Jha MK, Chowdhury A, Chowdary VM, Peiffer S (2007) Groundwater management and development by integrated remote sensing and geographic information systems: prospects and constraints. Water Resour Manag 21:427–467

    Article  Google Scholar 

  • Kim HG, Lee DK, Park C, Kil S, Son Y, Park JH (2015) Evaluating landslide hazards using RCP 4.5 and 8.5 scenarios. Environ Earth Sci 73(3):1385–1400

    Article  Google Scholar 

  • Kleidon A, Malhi Y, Cox PM (2010) Maximum entropy production in environmental and ecological systems. Philos Trans R Soc B 365(1545):1297–1302

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Kornejady A, Ownegh M, Rahmati O, Bahremand A (2017b) Landslide susceptibility assessment using three bivariate models considering the new topo-hydrological factor: HAND. Geocarto Int. https://doi.org/10.1080/10106049.2017.1334832

    Article  Google Scholar 

  • Kumar S, Stohlgren TJ (2009) Maxent modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia. J Ecol Nat Environ 1(4):094–098

    Google Scholar 

  • Liu Y, Guo Q, Tian Y (2012) A software framework for classification models of geographical data. Comput Geosci 42:47–56

    Article  Google Scholar 

  • Loiselle BA, Jørgensen PM, Consiglio T, Jiménez I, Blake JG, Lohmann LG, Montiel OM (2008) Predicting species distributions from herbarium collections, does climate bias in collection sampling influence model outcomes? J Biogeogr 35 (1): 105–116

    Google Scholar 

  • Magesh NS, Chandrasekar N, Soundranayagam JP (2012) Delineation of groundwater potential zones in Theni district, Tamil Nadu, Environ Earth Sci using remote sensing, GIS and MIF techniques. Geosci Front 3(2):189–196

    Article  Google Scholar 

  • Mair A, El-Kadi AI (2013) Logistic regression modeling to assess groundwater vulnerability to contamination in Hawaii, USA. J Contam Hydrol 153:1–23. https://doi.org/10.1016/j.jconhyd.2013.07.004

    Article  Google Scholar 

  • Manap MA, Sulaiman WNA, Ramli MF, Pradhan B, Surip N (2013) A knowledge-driven GIS modeling technique for groundwater potential mapping at the Upper Langat Basin, Malaysia Arab. J Geosci 6:1621–1637. https://doi.org/10.1007/s12517-011-0469-2

    Article  Google Scholar 

  • McCarthy E, Moretti D, Thomas L, DiMarzio N, Morrissey R, Jarvis S et al (2011) Changes in spatial and temporal distribution and vocal behavior of Blainville’s beaked whales (Mesoplodon densirostris) during multiship exercises with mid-frequency sonar. Mar Mamm Sci 27(3):E206–E226

    Article  Google Scholar 

  • Medley KA (2010) Niche shifts during the global invasion of the Asian tiger mosquito, Aedes albopictus Skuse (Culicidae), revealed by reciprocal distribution models. Glob Ecol Biogeogr 19(1):122–133

    Article  Google Scholar 

  • Mert A, Özkan K, Şentürk Ö, Negiz MG (2016) Changing the potential distribution of Turkey oak (Quercus cerris L.) under climate change in Turkey. Pol J Environ Stud 25(4):1633–1638

    Article  Google Scholar 

  • Metz CE (1978) Basic principles of ROC analysis. Semin Nucl Med 8(4):283–298 (WB Saunders)

    Article  Google Scholar 

  • Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modeling: a review of hydrological, geomorphological and biological applications. Hydro Process 5:3–30

    Article  Google Scholar 

  • Moosavi V, Niazi Y (2016) Development of hybrid wavelet packet-statistical models (WP-SM) for landslide susceptibility mapping. Landslides 13(1):97–114

    Article  Google Scholar 

  • Moreno R, Zamora R, Molina JR, Vasquez A, Herrera M (2011) Predictive modeling of microhabitats for endemic birds in South Chilean temperate forests using Maximum entropy (Maxent). Ecol Inform 6(6):364–370

    Article  Google Scholar 

  • Naghibi A, Pourghasemi HR (2015) A comparative assessment between three machine learning models and their performance comparison by bivariate and multivariate statistical methods for groundwater potential mapping in Iran. Water Resour Manag 29(14):5217–5236. https://doi.org/10.1007/s11269-015-1114-8

    Article  Google Scholar 

  • Naghibi SA, Pourghasemi HR, Pourtaghi ZS, Rezaei A (2015) Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran. Earth Sci Inf 8(1):171–186

    Article  Google Scholar 

  • Naghibi SA, Pourghasemi HR, 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. https://doi.org/10.1007/s10661-015-5049-6

    Article  Google Scholar 

  • Naghibi SA, Ahmadi K, Daneshi A (2017a) Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping. Water Resour Manag 31:2761

    Article  Google Scholar 

  • Naghibi SA, Moghaddam DD, Kalantar B, Pradhan B, Kisi O (2017b) A comparative assessment of GIS-based data mining models and a novel ensemble model in groundwater well potential mapping. J Hydrol 548:471–483

    Article  Google Scholar 

  • Naghibi SA, Pourghasemi HR, Abbaspour K (2018) A comparison between ten advanced and soft computing models for groundwater qanat potential assessment in Iran using R and GIS. Theor Appl Climatol 131(3–4):967–984

    Article  Google Scholar 

  • Nampak H, Pradhan B, Manap MA (2014) Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. J Hydrol. https://doi.org/10.1016/j.jhydrol.2014.02.053

    Article  Google Scholar 

  • Neshat A, Pradhan B, Pirasteh S, Shafri HZM (2014) Estimating groundwater vulnerability to pollution using a modified DRASTIC model in the Kerman agricultural area, Iran. Environ Earth Sci 71(7):3119–3131

    Article  Google Scholar 

  • Ng AY, Jordan MI (2001) On discriminative versus generative classifiers, a comparison of logistic regression and naive Bayes. Adv Neural Inf Process Syst 14:605–610

    Google Scholar 

  • Niamir A, Skidmore AK, Toxopeus AG, Munoz AR, Real R (2011) Finessing atlas data for species distribution models. Divers Distrib 17(6):1173–1185

    Article  Google Scholar 

  • Nieves V, Wang J, Bras RL (2011) Statistics of multifractal processes using the maximum entropy method. Geophys Res Lett 38:17

    Google Scholar 

  • 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. J Hydrol 411:290–308

    Article  Google Scholar 

  • Park NW (2015) Using maximum entropy modeling for landslide susceptibility mapping with multiple geoenvironmental data sets. Environ Earth Sci 73(3):937–949

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Perrier E, Salkini AB (1991) Supplemental Irrigation in the Near East and North Africa. Kluwer Academic Publisher, Norwell

    Book  Google Scholar 

  • Peterson AT (2011) Ecological niches and geographic distributions (MPB-49), vol 49. Princeton University Press, Princeton

    Google Scholar 

  • Phillips S (2012) A brief tutorial on Maxent. Lessons Conserv 3:107–135

    Google Scholar 

  • Phillips SJ, Dudík M (2008) Modeling of species distributions with Maxent, new extensions and a comprehensive evaluation. Ecography 31(2):161–175

    Article  Google Scholar 

  • Phillips SJ, Dudík M, Schapire RE (2004) A maximum entropy approach to species distribution modeling. In: Proceedings of the twenty-first international conference on machine learning. ACM, New York, p. 83

  • Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190(3):231–259

    Article  Google Scholar 

  • Phillips SJ, Dudík M, Elith J, Graham CH, Lehmann A, Leathwick J, Ferrier S (2009) Sample selection bias and presence-only distribution models, implications for background and pseudo-absence data. Ecol Appl 19(1):181–197

    Article  Google Scholar 

  • Pontius RG, Schneider LC (2001) Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agric Ecosyst Environ 85(1):239–248

    Article  Google Scholar 

  • Pourghasemi HR, Kerle N (2016) Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province. Iran Environ Earth Sci 75(3):1–17

    Google Scholar 

  • Pourghasemi HR, 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

    Article  Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR (2013) Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arab J Geosci 6(7):2351–2365

    Article  Google Scholar 

  • Pradhan B (2009) Groundwater potential zonation for basaltic watersheds using satellite remote sensing data and GIS techniques. Cent Eur J Geosci 1(1):120–129

    Google Scholar 

  • Pueyo S, He F, Zillio T (2007) The maximum entropy formalism and the idiosyncratic theory of biodiversity. Ecol Lett 10(11):1017–1028

    Article  Google Scholar 

  • Quinn SA, Gibbs JP, Hall MH, Petokas PJ (2013) Multi scale factors influencing distribution of the eastern hellbender salamander (Cryptobranchus alleganiensis alleganiensis) in the northern segment of its range. J Herpetol 47(1):78–84

    Article  Google Scholar 

  • Rahmati O, Melesse AM (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. Sci Total Environ 568:1110–1123

    Article  Google Scholar 

  • Rahmati O, Haghizadeh A, Stefanidis S (2016a) Assessing the accuracy of GIS-based analytical hierarchy process for watershed prioritization; Gorganrood River Basin, Iran. Water Resour Manag 30(3):1131–1150

    Article  Google Scholar 

  • Rahmati O, Pourghasemi HR, Melesse AM (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

    Article  Google Scholar 

  • Reddy S, Dávalos LM (2003) Geographical sampling bias and its implications for conservation priorities in Africa. J Biogeogr 30(11):1719–1727

    Article  Google Scholar 

  • Robertson MP, Cumming GS, Erasmus BFN (2010) Getting the most out of atlas data. Divers Distrib 16(3):363–375

    Article  Google Scholar 

  • Rodriguez-Galiano V, Mendes MP, Garcia-Soldado MJ, Chica-Olmo M, Ribeiro L (2014) Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: a case study in an agricultural setting (Southern Spain). Sci Total Environ 476–477:189–206

    Article  Google Scholar 

  • Ross SM (2014) Introduction to probability models. Academic Press, Orlando

  • Sarkar B, Deota B, Raju P, Jugran D (2001) A geographic information system approach to evaluation of groundwater potentiality of Shamri micro watershed in the Shimla Taluk, Himachal Pradesh. J Indian Soc Remote Sens 29(3):151–164

    Article  Google Scholar 

  • Saud MA (2010) Mapping potential areas for groundwater storage in Wadi Aurnah Basin, western Arabian Peninsula, using remote sensing and geographic information system techniques. Hydrogeol J 18:1481–1495

    Article  Google Scholar 

  • Senthil Kumar GR, Shankar K (2014) Assessment of groundwater potential zones using GIS. Front Geosci 2(1):1–10

    Google Scholar 

  • Shahid S, Nath SK, Roy J (2000) Groundwater potential modeling in a soft rock area using a GIS. Int J Remote Sens 21(9):1919–1924

    Article  Google Scholar 

  • Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423, 623–656 [Mathematical Reviews (MathSciNet), MR10, 133e]

  • Shannon CE (1951) Prediction and entropy of printed English. Bell Syst Tech J 30(1):50–64

    Article  Google Scholar 

  • Shen G, Pimm SL, Feng C, Ren G, Liu Y, Xu W et al (2015) Climate change challenges the current conservation strategy for the giant panda. Biol Conserv 190:43–50

    Article  Google Scholar 

  • Sidle R, Ochiai H (2006) Processes, prediction, and land use. Water resources monograph. American Geophysical Union, Washington

    Book  Google Scholar 

  • Singh AK, Prakash SR (2002) An integrated approach of remote sensing, geophysics and GIS to evaluation of groundwater potentiality of Ojhala sub-watershed, Mirjapur district, UP, India. In: Asian conference on GIS, GPS, aerial photography and remote sensing, Bangkok, Thailand

  • Sorichetta A, Ballabio C, Masetti M, Robinson GR Jr, Sterlacchini S (2013) A comparison of data-driven groundwater vulnerability assessment methods. Ground Water 51(6):866–879. https://doi.org/10.1111/gwat.12012

    Article  Google Scholar 

  • Storey RG, Howard KWF, Williams DD (2003) Factors controlling riffle-scale hyporheic exchange flows and their seasonal changes in a gaining stream: a three-dimensional groundwater flow model. Water Resour Res 39(2):1034

    Article  Google Scholar 

  • Subyani A (2005) Hydrochemical identification and salinity problem of ground-water in Wadi Yalamlam basin, Western Saudi Arabia. J Arid Environ 60:53–66

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Townsend Peterson A, Papeş M, Eaton M (2007) Transferability and model evaluation in ecological niche modeling, a comparison of GARP and Maxent. Ecography 30(4):550–560

    Article  Google Scholar 

  • Vapnik VN, Vapnik V (1998) Statistical learning theory. Wiley, New York

    Google Scholar 

  • Veloz SD (2009) Spatially autocorrelated sampling falsely inflates measures of accuracy for presence-only niche models. J Biogeogr 36(12):2290–2299

    Article  Google Scholar 

  • Wahyudi AD, Bartzke M, Küster E, Bogaert P (2012) Maximum entropy estimation of a benzene contaminated plume using ecotoxicological assays. Environ Pollut 172C:170–179

    Google Scholar 

  • Wang J, Bras RL (2011) A model of evapotranspiration based on the theory of maximum entropy production. Water Resour Res 47(3):1–10. https://doi.org/10.1029/2010WR009392

    Article  Google Scholar 

  • Warren DL, Seifert SN (2011) Ecological niche modeling in Maxent, the importance of model complexity and the performance of model selection criteria. Ecol Appl 21(2):335–342

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Wisz MS, Hijmans RJ, Li J, Peterson AT, Graham CH, Guisan A (2008) Effects of sample size on the performance of species distribution models. Divers Distrib 14(5):763–773

    Article  Google Scholar 

  • Wollan AK, Bakkestuen V, Kauserud H, Gulden G, Halvorsen R (2008) Modeling and predicting fungal distribution patterns using herbarium data. J Biogeogr 35(12):2298–2310

    Article  Google Scholar 

  • Wolmarans R, Robertson MP, van Rensburg BJ (2010) Predicting invasive alien plant distributions, how geographical bias in occurrence records influences model performance. J Biogeogr 37(9):1797–1810

    Article  Google Scholar 

  • Yackulic CB, Chandler R, Zipkin EF, Royle JA, Nichols JD, Campbell Grant EH, Veran S (2013) Presence-only modelling using MAXENT, when can we trust the inferences? Methods Ecol Evol 4(3):236–243

    Article  Google Scholar 

  • Yu J, Wang C, Wan J, Han S, Wang Q, Nie S (2014) A model-based method to evaluate the ability of nature reserves to protect endangered tree species in the context of climate change. For Ecol Manag 327:48–54

    Article  Google Scholar 

  • Zabihi M, Pourghasemi HR, Sadat Pourtaghi Z, Behzadfar M (2016) GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran. Environ Earth Sci 75:665

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ali Golkarian.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Golkarian, A., Rahmati, O. Use of a maximum entropy model to identify the key factors that influence groundwater availability on the Gonabad Plain, Iran. Environ Earth Sci 77, 369 (2018). https://doi.org/10.1007/s12665-018-7551-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12665-018-7551-y

Keywords

Navigation