Development of a GIS-based catastrophe theory model (modified DRASTIC model) for groundwater vulnerability assessment

Development of a GIS-based catastrophe theory model (modified DRASTIC model) for groundwater... This study developed a new paradigm for groundwater vulnerability assessment by modifying the standard DRASTIC index (DI) model based on catastrophe theory. The developed paradigm was called the catastrophe theory-based DI (CDI) model. The proposed model was applied to assess groundwater vulnerability to pollution index (GVPI) in Perak Province, Malaysia. The area vulnerability index was modeled by considering the DRASTIC multiple vulnerability causative factors (VCFs) obtained from different data sources. The weights and ranking of the VCFs were computed by using the inner fuzzy membership mechanism of the CDI model. The estimated vulnerability index values of the CDI model were processed in a geographic information system (GIS) environment to produce a catastrophe theory–DRASTIC groundwater vulnerability to pollution index (CDGVPI) map, which demarcated the area into five vulnerability zones. The produced CDGVPI map was validated by applying the water quality status–vulnerability zone relationship (WVR) approach and the relative operating characteristic (ROC) curve method. The performance of the developed CDI model was compared with that of the standard DI model. The validation results of the WVR approach exhibits 89.29% prediction accuracy for the CDI model compared with 75% for the DI model. Meanwhile, the ROC validation results for the CDI and DI models are 88.8% and 78%, respectively. The GIS-based CDI model demonstrated better performance than the DI model. The GVPI maps produced in this study can be used for precise decision making process in environmental planning and groundwater management. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Earth Science Informatics Springer Journals

Development of a GIS-based catastrophe theory model (modified DRASTIC model) for groundwater vulnerability assessment

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by Springer-Verlag Berlin Heidelberg
Subject
Earth Sciences; Earth Sciences, general; Information Systems Applications (incl.Internet); Simulation and Modeling; Ontology
ISSN
1865-0473
eISSN
1865-0481
D.O.I.
10.1007/s12145-017-0300-z
Publisher site
See Article on Publisher Site

Abstract

This study developed a new paradigm for groundwater vulnerability assessment by modifying the standard DRASTIC index (DI) model based on catastrophe theory. The developed paradigm was called the catastrophe theory-based DI (CDI) model. The proposed model was applied to assess groundwater vulnerability to pollution index (GVPI) in Perak Province, Malaysia. The area vulnerability index was modeled by considering the DRASTIC multiple vulnerability causative factors (VCFs) obtained from different data sources. The weights and ranking of the VCFs were computed by using the inner fuzzy membership mechanism of the CDI model. The estimated vulnerability index values of the CDI model were processed in a geographic information system (GIS) environment to produce a catastrophe theory–DRASTIC groundwater vulnerability to pollution index (CDGVPI) map, which demarcated the area into five vulnerability zones. The produced CDGVPI map was validated by applying the water quality status–vulnerability zone relationship (WVR) approach and the relative operating characteristic (ROC) curve method. The performance of the developed CDI model was compared with that of the standard DI model. The validation results of the WVR approach exhibits 89.29% prediction accuracy for the CDI model compared with 75% for the DI model. Meanwhile, the ROC validation results for the CDI and DI models are 88.8% and 78%, respectively. The GIS-based CDI model demonstrated better performance than the DI model. The GVPI maps produced in this study can be used for precise decision making process in environmental planning and groundwater management.

Journal

Earth Science InformaticsSpringer Journals

Published: Apr 1, 2017

References

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