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GIS-based multi-criteria decision making and entropy approaches for groundwater potential zones delineation

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Abstract

In arid and semi-arid, due to the lack of surface water, groundwater plays key role in people survival. Therefore, assessing groundwater potential zones is vital to conserve and manage this resource properly. The main object of this study was to compare two methods of AHP and entropy for delineating groundwater potential zone in arid region of Iran. For this regard, withdraw in groundwater level in 16 piezometer wells along with eight GWP conditioning factors of slope degree, stream power index (SPI), landuse, geology; rainfall volume, elevation, soil and lineament density have been considered in the training process while the discharge in the other 16 wells was involved in verifying process of the two models. GWP maps were provided by overlaying all the thematic maps in terms of the obtained weights from AHP and entropy methods. Finally, the models performance was compared through statistical measures of Sensivity, Specificity and True Skill Statistics (TSS). The results show that IOE model (TSS = 0.16) have higher prediction performance than AHP approach (TSS = 0). Therefore, IOE was more promising tools for GWP modeling.

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References

  • Abu-alnaeem MF, Yusoff I, Fatt NTF, Alias Y, Raksmey M (2018) Assessment of groundwater salinity and quality in Gaza coastal aquifer, Gaza Strip, Palestine: An integrated statistical, geostatistical and hydrogeochemical approaches study. Sci Total Environ 615:972–989

    Article  Google Scholar 

  • Acharya T, Nag SK (2013) Study of groundwater prospects of the crystalline rocks in Purulia District, West Bengal, India using remote sensing data. Earth Resources 1(2):54–59

    Article  Google Scholar 

  • Al-Abadi AM, Pourghasemi HR, Shahid S, Ghalib HB (2017) Spatial mapping of groundwater potential using entropy weighted linear aggregate novel approach and GIS. Arab J Geosci 42(3):1185–1199

    Google Scholar 

  • Allouche O, Tsoar A, Kadmon R (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J Appl Ecol 43(6):1223–1232

    Article  Google Scholar 

  • Andualem TG, Demeke GG (2019) Groundwater potential assessment using GIS and remote sensing: a case study of Guna tana landscape, upper blue Nile Basin, Ethiopia. J Hydrol Reg Stud 24:100610

    Article  Google Scholar 

  • Arulbalaji P, Padmalal D, Sreelash K (2019) GIS and AHP techniques based delineation of groundwater potential zones: a case study from southern western Ghats, India. Sci Rep 9(1):1–17

    Article  Google Scholar 

  • Ashrafzadeh A, Roshandel F, Khaledian MR, Vazifedoust M, Rezaei M (2016) Assessment of groundwater salinity risk using kriging methods: a case study in northern Iran. Agric Water Manag 178:215–224

    Article  Google Scholar 

  • Bhattacharya S, Das S, Das S, Kalashetty M, Warghat SR (2020) An integrated approach for mapping groundwater potential applying geospatial and MIF techniques in the semiarid region. Environ Dev Sustain:1–16

  • Bui DT, Hoang ND, Martínez-Álvarez F, Ngo PTT, Hoa PV, Pham TD, Samui P, Costache R (2020) A novel deep learning neural network approach for predicting flash flood susceptibility: a case study at a high frequency tropical storm area. Sci Total Environ 701:134413

    Article  Google Scholar 

  • Chen CY, Chang JM (2016) Landslide dam formation susceptibility analysis based on geomorphic features. Landslides 13:1019–1033

    Article  Google Scholar 

  • Chitsaz N, Azarnivand A (2016) Water scarcity management in arid regions based on an extended multiple criteria technique. Water Resour Manage:1–18

  • Constantin M, Bednarik M, Jurchescu MC, Vlaicu M (2011) Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environ Earth Sci 63:397–406

    Article  Google Scholar 

  • Das B, Pal SC, Malik S, Chakrabortty R (2019) Modeling groundwater potential zones of Puruliya district, West Bengal, India using remote sensing and GIS techniques. Geol Ecol Landscapes 3(3):223–237

    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 cost of India. Int J Remote Sens 28(24):5583–5601

    Article  Google Scholar 

  • Dingman LS (2015) Physical hydrology. Waveland press Inc.

  • Elgettafi M, Elmandour A, Himi M, Casas Ponsatí A, Elhaouadi B (2012) Messinian salinity crisis impact on the groundwater quality in Kert aquifer NE Morocco, hydrogeochemical and statistical approaches. Int J Water Resour Environ Eng 4(11):339–351

    Google Scholar 

  • Fukuda S, De Baets B, Waegeman W, Verwaeren J, Mouton AM (2013) Habitat prediction and knowledge extraction for spawning European grayling (Thymallus thymallus L.) using a broad range of species distribution models. Environ Modell Softw 47:1–6

    Article  Google Scholar 

  • Guru B, Seshan K, Bera S (2017) Frequency ratio model for groundwater potential mapping and its sustainable management in cold desert, India. J King Saud Univ Sci 29(3):333–347

    Article  Google Scholar 

  • Habibi V, Ahmadi H, Jafari M, Moeini A (2019) Application of nonlinear models and groundwater index to predict desertification case study: Sharifabad watershed. Nat Hazards (Dordr) 99(2):715–733

    Article  Google Scholar 

  • Hardcastle KC (1995) Photolineament factor: a new computer-aided method for remotely sensing the degree to which bedrock is fractured. Photogramm Eng Remote Sensing 61(6):739–747

    Google Scholar 

  • Jamshidzadeh Z, Mirbagheri SA (2011) Evaluation of groundwater quantity and quality in the Kashan Basin, Central Iran. Desalination 270:23–30

    Article  Google Scholar 

  • 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 

  • Kaur L, Rishi MS, Singh G, Thakur SN (2020) Groundwater potential assessment of an alluvial aquifer in Yamuna sub-basin (Panipat region) using remote sensing and GIS techniques in conjunction with analytical hierarchy process (AHP) and catastrophe theory (CT). Ecol Indic 110:105850

    Article  Google Scholar 

  • Khoshtinat S, Aminnejad B, Hassanzadeh Y, Ahmadi H (2019) Groundwater potential assessment of the Sero plain using bivariate models of the frequency ratio, Shannon entropy and evidential belief function. J Earth Syst Sci 128(6):152

    Article  Google Scholar 

  • Khosravi K, Panahi M, Tien Bui D (2018) Spatial prediction of groundwater spring potential mapping based on adaptive neuro-fuzzy inference system and metaheuristic optimization. Hydrol Earth Syst Sci 22:4771–4792

    Article  Google Scholar 

  • Kindie AT, Enku T, Moges MA, Geremew BS, Atinkut HB (2018) Spatial analysis of groundwater potential using GIS based multi criteria decision analysis method in Lake Tana Basin, Ethiopia, In international conference on advances of science and technology. Springer, Cham, pp 439–456

    Google Scholar 

  • Kumar VA, Mondal NC, Ahmed S (2020) Identification of groundwater potential zones using RS, GIS and AHP techniques: a case study in a part of Deccan volcanic province (DVP), Maharashtra, India. J Indian Soc Remote Sens:1–15

  • Ledesma-Ruiz R, Pastén-Zapata E, Parra R, Harter T, Mahlknecht J (2015) Investigation of the geochemical evolution of groundwater under agricultural land: a case study in northeastern Mexico. J Hydrol 521:410–423

    Article  Google Scholar 

  • Lee S, Hong SM, Jung HS (2018) GIS-based groundwater potential mapping using artificial neural network and support vector machine models: the case of Boryeong city in Korea. Geocarto Int 33(8):847–861

    Article  Google Scholar 

  • Lee S, Kim YS, Oh HJ (2012) Application of a weights-of-evidence method and GIS to regional groundwater productivity potential mapping. J Environ Manag 96:91–105

    Article  Google Scholar 

  • Li P, Wu J, Qian H (2016) Hydrochemical appraisal of groundwater quality for drinking and irrigation purposes and the major influencing factors: a case study in and around Hua County, China. Arab J Geosci 9(1):1–17

    Article  Google Scholar 

  • Lu GY, Wong DW (2008) An adoptive inverse-distance weighting spatial interpolation techniques. Comput Geosci-UK 34:1044–1055

    Article  Google Scholar 

  • Machiwal D, Rangi N, Sharma A (2015) Integrated knowledge and data-driven approaches for groundwater potential zoning using GIS and multi-criteria decision making techniques on hardrock terrain of Ahar catchment, Rajasthan, India. Environ Earth Sci 73(4):1871–1892

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Mogheir Y, De Lima JLMP, Singh VP (2005) Assessment of informativeness of groundwater monitoring in developing regions (Gaza strip case study), water Resour. Manage 19:737–757

    Google Scholar 

  • Mohammadi F, Ehteshami M, Mirbagheri SA, Tavassoli S (2017) Monitoring groundwater and its suitability for drinking and irrigation purposes in the Sharif Abad Basin, Central Iran. World J Eng Technol 5(3):487–506

    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 Informatics 8:171–186

    Article  Google Scholar 

  • Nguyen PT, Ha DH, Jaafari A, Nguyen HD, Van Phong T, Al-Ansari N, Prakash I, Le HV, Pham BT (2020) Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam. Int J Environ Res Public Health 17(7):2473

    Article  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 

  • Pinto D, Shrestha S, Babel MS, Ninsawat S (2017) Delineation of groundwater potential zones in theComoro watershed, Timor Leste using GIS, remote sensing and analytic hierarchy process (AHP) technique. Appl Water Sci 7:503–519

    Article  Google Scholar 

  • Pourghasemi HR, Beheshtirad M (2015) Assessment of a data-driven evidential belief function model and GIS for groundwater potential mapping in the Koohrang watershed, Iran. Geocarto Int 30:662–685

    Article  Google Scholar 

  • Pourtaghi ZS, Pourghasemi HR (2014) GIS-based groundwater spring potential assessment and mapping in the Birjand township, southern Khorasan Province, Iran. Hydrogeol J 22:643–662

    Article  Google Scholar 

  • Razandi Y, Pourghasemi HR, Neisani NS, Rahmati O (2015) Application of analytical hierarchy process, frequency ratio, and certainty factor models for groundwater potential mapping using GIS. Earth Sci Informatics 8:867–883

    Article  Google Scholar 

  • Saaty TL (1980) The analytic hierarchy process. Mc Graw Hill Company, New York

    Google Scholar 

  • Shaban A, Khawlie M, Abdallah C (2006) Use of remote sensing and GIS to determine recharge potential zones: the case of occidental Lebanon. Hydrogeol J 14(4):433–443

    Article  Google Scholar 

  • Shahid S, Nath S, 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 

  • Sisay L (2007) Application of remote sensing and GIS for groundwater potential zone mapping in northern Ada’a plain (Modjo catchment) university. Addis Ababa University

  • Thapa R, Gupta S, Guin S, Kaur H (2017) Assessment of groundwater potential zones using multi-influencing factor (MIF) and GIS: a case study from Birbhum district, West Bengal. Appl Water Sci 7:4117–4131

    Article  Google Scholar 

  • Theil H (1972) Statistical decomposition analysis. North-Holland Publishing Company, Amsterdam

    Google Scholar 

  • Vengosh A, Kloppmann W, Marei A, Livshitz Y, Gutierrez A, Banna M, Raanan H (2005) Sources of salinity and boron in the Gaza strip: natural contaminant flow in the southern Mediterranean coastal aquifer. Water Resour Res 41(1)

  • Wu J, Sun Z (2016) Evaluation of shallow groundwater contamination and associated human health risk in an alluvial plain impacted by agricultural and industrial activities, mid-West China. Expo Health 8(3):311–329

    Article  Google Scholar 

  • Xu C, Xu X, Dai F, Xiao J (2012) Landslide hazard mapping using GIS and weight of evidence model in Qingshui River watershed of 2008 Wenchuan earthquake struck region. J Earth Sci 23(1):97–120

    Article  Google Scholar 

  • Yufeng S, Fengxiang J (2009) Landslide stability analysis based on generalized information entropy, 2009 International Conference on Environmental Science and Information Application Technology, pp 83–85

    Google Scholar 

  • Zare M, Pourghasemi H, Vafakhah M, Pradhan B (2013) Landslide susceptibility mapping at Vaz watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms. Arab J Geosci 6(8):2873–2888

    Article  Google Scholar 

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Acknowledgements

I would like to thank Qom Regional Water Company helping us to provide information for this research.

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  1. Department of Agriculture, Payame Noor University, Tehran, Iran

    Elham Forootan

  2. Department of Natural Resource and Watershed Management, Qom, Iran

    Fakhrossadat Seyedi

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  1. Elham Forootan
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Correspondence to Elham Forootan.

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Forootan, E., Seyedi, F. GIS-based multi-criteria decision making and entropy approaches for groundwater potential zones delineation. Earth Sci Inform 14, 333–347 (2021). https://doi.org/10.1007/s12145-021-00576-8

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