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Groundwater vulnerability mapping: A GIS and fuzzy rule based integrated tool [An article from: Applied Geography]
Book Details
Author(s)B. Dixon
PublisherElsevier
ISBN / ASINB000RR6F8W
ISBN-13978B000RR6F80
AvailabilityAvailable for download now
Sales Rank13,091,192
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from Applied Geography, published by Elsevier in . The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
Contamination of groundwater has become a major concern in recent years. Since testing of water quality of all domestic and irrigation wells within large watersheds is not economically feasible, one frequently used monitoring strategy is to develop contamination potential maps of groundwater, and then prioritize those wells located in the potentially highly contaminated areas for testing of contaminants. However, generation of contamination potential maps based on groundwater sensitivity and vulnerability is not an easy task due inherent uncertainty. Therefore, the overall goal of this research is to improve the methodology for the generation of contamination potential maps by using detailed landuse/pesticide and soil structure information in conjunction with selected parameters from the DRASTIC model. The specific objectives of this study are (i) to incorporate GIS, GPS, remote sensing and the fuzzy rule-based model to generate groundwater sensitivity maps, and (ii) compare the results of our new methodologies with the modified DRASTIC Index (DI) and field water quality data. In this study, three different models were developed (viz. DI"f"u"z"z, VI"f"u"z"z and VI"f"u"z"z"_"p"e"d) and were compared to the DI. Once the preliminary fuzzy logic-based (DI"f"u"z"z) was generated using selected parameters from DI, the methodology was further refined through VI"f"u"z"z and VI"f"u"z"z"_"p"e"d models that incorporated landuse/pesticide application and soil structure information, respectively. This study was conducted in Woodruff County of the Mississippi Delta region of Arkansas. Water quality data for 55 wells were used to evaluate the contamination potential maps. The sensitivity map generated by VI"f"u"z"z"_"p"e"d with soil structure showed significantly better coincidence results when compared with the field data.
Description:
Contamination of groundwater has become a major concern in recent years. Since testing of water quality of all domestic and irrigation wells within large watersheds is not economically feasible, one frequently used monitoring strategy is to develop contamination potential maps of groundwater, and then prioritize those wells located in the potentially highly contaminated areas for testing of contaminants. However, generation of contamination potential maps based on groundwater sensitivity and vulnerability is not an easy task due inherent uncertainty. Therefore, the overall goal of this research is to improve the methodology for the generation of contamination potential maps by using detailed landuse/pesticide and soil structure information in conjunction with selected parameters from the DRASTIC model. The specific objectives of this study are (i) to incorporate GIS, GPS, remote sensing and the fuzzy rule-based model to generate groundwater sensitivity maps, and (ii) compare the results of our new methodologies with the modified DRASTIC Index (DI) and field water quality data. In this study, three different models were developed (viz. DI"f"u"z"z, VI"f"u"z"z and VI"f"u"z"z"_"p"e"d) and were compared to the DI. Once the preliminary fuzzy logic-based (DI"f"u"z"z) was generated using selected parameters from DI, the methodology was further refined through VI"f"u"z"z and VI"f"u"z"z"_"p"e"d models that incorporated landuse/pesticide application and soil structure information, respectively. This study was conducted in Woodruff County of the Mississippi Delta region of Arkansas. Water quality data for 55 wells were used to evaluate the contamination potential maps. The sensitivity map generated by VI"f"u"z"z"_"p"e"d with soil structure showed significantly better coincidence results when compared with the field data.
