2016
DOI: 10.1007/s11269-016-1440-5
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Localization of Groundwater Vulnerability Assessment Using Catastrophe Theory

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Cited by 55 publications
(12 citation statements)
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“…A catastrophe evaluation method quantifies the relative importance of indices according to the internal contradictions and mechanisms in normalization equations of the system, and reduces the subjective factors in the evaluation process effectively. The method has been widely used in several research fields, including water resources assessment [25,26], mapping floods susceptibility [27], safety analysis of railway systems [28], and quantitative analysis of a non-equilibrium phase transition process [29].…”
Section: Common Catastrophe Evaluation Modelmentioning
confidence: 99%
“…A catastrophe evaluation method quantifies the relative importance of indices according to the internal contradictions and mechanisms in normalization equations of the system, and reduces the subjective factors in the evaluation process effectively. The method has been widely used in several research fields, including water resources assessment [25,26], mapping floods susceptibility [27], safety analysis of railway systems [28], and quantitative analysis of a non-equilibrium phase transition process [29].…”
Section: Common Catastrophe Evaluation Modelmentioning
confidence: 99%
“…They include GOD [7], DRASTIC [8], SINTACS [9] etc., which are selected on the basis of availability of data. DRASTIC is one of the better known and extensively used overlay index methods that is applied worldwide for groundwater vulnerability assessment [10][11][12]. It was developed by the United States Environment Protection Agency (USEPA).…”
Section: Introductionmentioning
confidence: 99%
“…One of the consequences of these limitations particularly the latter factor is such that the final output of DRASTIC index does not reflect the resultant effect of any missing data etc. Sequel to these aforementioned weaknesses of DRASTIC model, several researchers have devised different means of improving the model's performance (Thirumalaivasan et al 2003;Dixon 2005;Antonakos and Lambrakis 2007;Ckakraborty 2007;Pathak et al 2014;Pradhan et al 2013;Mogaji et al 2014;Nobre et al 2007;Chen et al 2013;Singh et al 2015;Nerantzis and Konstantinos 2015;Wang et al 2012;Biswajeet and Pradhan 2104;Boris et al 2016;Kumar et al 2017;Issoufou and Defourny 2016;Sadeghfam et al 2016). However, few of these DRASTIC model enhancement studies have quantitatively evaluated the efficiency of their improved model output versus the conventional DRASTIC model result.…”
Section: Introductionmentioning
confidence: 99%