Assessment of a data-driven evidential belief function model and GIS for groundwater potential mapping in the Koohrang Watershed, Iran

Geocarto International

Volumn 30, Issue 6, 2015, Pages 662-685

  Pourghasemi, Hamid Reza ^a   Beheshtirad, Masood ^b  

^a SHIRAZ UNIVERSITY   (Iran)

^b ISLAMIC AZAD UNIVERSITY   (Iran)

Author keywords

evidential belief function; GIS; groundwater potential mapping; Iran

Indexed keywords

GEOLOGICAL MAPPING; GIS; GROUNDWATER; LAND USE PLANNING; NUMERICAL MODEL; PERFORMANCE ASSESSMENT; WATER MANAGEMENT; WATER PLANNING; WATERSHED;

CHAHAR MAHALL VA BAKHTIARI; IRAN;

EID: 84929507617     PISSN: 10106049     EISSN: None     Source Type: Journal    
DOI: 10.1080/10106049.2014.966161     Document Type: Article

References (48)

1. A.Akgun, E.A.Sezer, H.A.Nefeslioglu, C.Gokceoglu, B.Pradhan. 2012. An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci. 38:23–34.10.1016/j.cageo.2011.04.012
2. O.F.Althuwaynee, B.Pradhan, H.J.Park, 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. 114:21–36.10.1016/j.catena.2013.10.011
3. S.A.Assadollahi. 2009. Groundwater resources management in Iran. Secretary General, IRNCID & Deputy of Protection and Exploitation of Iran Water Resources Management Co.; p. 16.
4. M.Awawdeh, M.Obeidat, M.Al-Mohammad, K.Al-Qudah, R.Jaradat. 2014. Integrated GIS and remote sensing for mapping groundwater potentiality in the Tulul al Ashaqif Northeast Jordan. Arab J Geosci. 7:2377–2392.
5. M.H.Ayazi, S.Pirasteh, A.K.P.Arvin, B.Pradhan, B.Nikouravan, S.Mansor. 2010. Disasters and risk reduction in groundwater: Zagros Mountain Southwest Iran using geoinformatics techniques. Disaster Adv. 3:51–57.
6. K.Beven, M.J.Kirkby. 1979. A physically based, variable contributing area model of basin hydrology. Hydrol Sci Bull. 24:43–69.10.1080/02626667909491834
7. BGR. 2011. Federal institute for geosciences and natural resources; [cited 2011 Jul 12]. Available from: http://www.bgr.bund.de
8. E.J.M.Carranza, M.Hale. 2002. Evidential belief functions for data-driven geologically constrained mapping of gold potential, Baguio district, Philippines. Ore Geol Rev. 22:117–132.
9. E.J.M.Carranza, T.Woldai, E.M.Chikambwe. 2005. Application of data-driven evidential belief functions to prospectivity mapping for aquamarine-bearing pegmatites, Lundazi District, Zambia. Nat Resour Res. 14:47–63.10.1007/s11053-005-4678-9
10. E.J.M.Carranza, F.J.A.van Ruitenbeek, C.Hecker, M.van der Meijde, F.D.van der Meer. 2008. Knowledge-guided data-driven evidential belief modeling of mineral prospectivity in Cabo de Gata, SE Spain. Int J Appl Earth Obs Geoinf. 10:374–387.10.1016/j.jag.2008.02.008
11. F.Chung-Jo, A.G.Fabbri. 2003. Validation of spatial prediction models for landslide hazard mapping. Nat Hazards. 30:451–472.10.1023/B:NHAZ.0000007172.62651.2b
12. A.Corsini, F.Cervi, 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. 111:79–87.10.1016/j.geomorph.2008.03.015
13. D.Davoodi Moghaddam, M.Rezaei, H.R.Pourghasemi, Z.S.Pourtaghi, B.Pradhan. 2013. Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan watershed Iran. Arab J Geosci. doi:10.1007/s12517-013-1161-5.
14. A.P.Dempster. 1967. Upper and lower probabilities induced by a multivalued mapping. Ann Math Stat. 38:325–339.10.1214/aoms/1177698950
15. A.P.Dempster. 1968. Generalization of Bayesian inference. J R Stat Soc Series B. 30:205–247.
16. J.P.Egan. 1975. Signal detection theory and ROC analysis. New York (NY): Academic; p. 266–268.
17. T.R.Godebo. 2005. Application of remote sensing and GIS for geological investigation and groundwater potential zone identification, Southeastern Ethiopian Plateau, Bale Mountains and the surrounding areas [dissertation]. Addis A Baba University.
18. [GSI] Geology Survey of Iran. 1997. http://www.gsi.ir/Main/Lang_en/index.html
19. R.C.Heath. 1983. Basic ground-water hydrology. Geological Survey Water-Supply Paper 2220; 86 p.
20. S.Kaliraj, N.Chandrasekar, N.S.Magesh. 2014. Identification of potential groundwater recharge zones in Vaigai upper basin, Tamil Nadu, using GIS-based analytical hierarchical process (AHP) technique. Arab J Geosci. 7:1385–1401.
21. S.Lee, K.Y.Song, Y.Kim, I.Park. 2012a. Regional groundwater productivity potential mapping using a geographic information system (GIS) based artificial neural network model. Hydrogeol J. 20:1511–1527.10.1007/s10040-012-0894-7
22. S.Lee, Y.S.Kim, H.J.Oh. 2012b. Application of a weights-of-evidence method and GIS to regional groundwater productivity potential mapping. J Environ Manage. 96:91–105.10.1016/j.jenvman.2011.09.016
23. S.Lee, J.Hwang, I.Park. 2012c. Application of data-driven evidential belief functions to landslide susceptibility mapping in Jinbu, Korea. Catena. 100:15–30.
24. M.A.Manap, H.Nampak, B.Pradhan, S.Lee, W.N.A.Sulaiman, M.F.Ramli. 2014. Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS. Arab J Geosci. 7:711–724.
25. M.A.Manap, W.N.A.Sulaiman, M.F.Ramli, B.Pradhan, N.Surip. 2013. A knowledge driven GIS modeling technique for groundwater potential mapping at the Upper Langat Basin, Malaysia. Arab J Geosci. 6:1621–1637.10.1007/s12517-011-0469-2
26. M.Mohammady, H.R.Pourghasemi, B.Pradhan. 2012. Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster-Shafer, and weights of-evidence models. J Asian Earth Sci. 61:221–236.10.1016/j.jseaes.2012.10.005
27. H.R.Mojiri, A.R.Zarei. 2006. The investigation of precipitation condition in the Zagros area and its effects on the central plateau of Iran. The 2nd Conference of Water Resource Management. Tehran (Iran).
28. I.D.Moore, G.J.Burch. 1986. Sediment transport capacity of sheet and rill flow: application of unit stream power theory. Water Resour Res. 22:1350–1360.10.1029/WR022i008p01350
29. I.D.Moore, R.B.Grayson, A.R.Ladson. 1991. Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process. 5:3–30.10.1002/(ISSN)1099-1085
30. S.A.Naghibi, H.R.Pourghasemi, Z.S.Pourtaghi, A.Rezaei. 2014. Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed. Iran, Earth Sci Inform. doi: 10.1007/s12145-014-0145-7.
31. H.Nampak, B.Pradhan, M.A.Manap. 2014. Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. J Hydrol. 513:283–300.
32. M.Negnevitsky. 2002. Artificial intelligence: a guide to intelligent systems. King’s Lynn: Addison-Wesley/Pearson Education; 415 p.
33. A.Neshat, B.Pradhan, S.Pirasteh, H.Z.M.Shafri. 2014. Estimating groundwater vulnerability to pollution using a modified DRASTIC model in the Kerman agricultural area, Iran. Environ Earth Sci. 71:3119–3131.
34. R.M.O’brien. 2007. A caution regarding rules of thumb for variance inflation factors. Qual Quantity. 41:673–690.10.1007/s11135-006-9018-6
35. H.J.Oh, Y.S.Kim, J.K.Choi, E.Park, S.Lee. 2011. GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea. J Hydrol. 399:158–172.10.1016/j.jhydrol.2010.12.027
36. A.Ozdemir. 2011a. 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. 411:290–308.10.1016/j.jhydrol.2011.10.010
37. A.Ozdemir. 2011b. Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey). J Hydrol. 405:123–136.10.1016/j.jhydrol.2011.05.015
38. A.Ozdemir, T.Altural. 2013. A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. J Asian Earth Sci. 64:180–197.10.1016/j.jseaes.2012.12.014
39. N.W.Park. 2011. Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility analysis. Environ Earth Sci. 62:367–376.10.1007/s12665-010-0531-5
40. H.R.Pourghasemi, B.Pradhan, C.Gokceoglu. 2012. Remote sensing data derived parameters and its use in landslide susceptibility assessment using Shannon’s entropy and GIS. Applied Mechanics and Materials. 225:486–491.
41. H.R.Pourghasemi, H.R.Moradi, S.M.Fatemi Aghda. 2013. Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Nat Hazards. 69:749–779.
42. H.R.Pourghasemi, H.R.Moradi, S.M.Fatemi Aghda, C.Gokceoglu, B.Pradhan. 2014. GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran, Iran). Arab J Geosci. 7:1857–1878.
43. Z.S.Pourtaghi, H.R.Pourghasemi. 2014. GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province, Iran. Hydrogeol J. 22:643–662.
44. B.Pradhan. 2009. Groundwater potential zonation for basaltic watersheds using satellite remote sensing data and GIS techniques. Cent Eur J Geosci. 1:120–129.10.2478/v10085-009-0008-5
45. B.Pradhan, M.H.Abokharima, M.Neamah Jebur, M.Shafapour Tehrany. 2014. Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Nat Hazards. 73:1019–1042.
46. K.Ravilious. 2008. Iran sinking as groundwater resources disappears. http://news.nationalgeographic.com/news/2008/09/080922-iran-groundwater.html.
47. G.Shafer. 1976. A mathematical theory of evidence. Vol. 1. Princeton, NJ: Princeton University Press.
48. D.Tien Bui, B.Pradhan, O.Lofman, I.Revhaug, O.B.Dick. 2012. Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): A comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena. 96:28–40.10.1016/j.catena.2012.04.001