Title

A case study using support vector machines, neural networks and logistic regression in a GIS to predict wells contaminated with nitrate-N.

SelectedWorks Author Profiles:

Barnali Dixon

Document Type

Article

Publication Date

2009

Date Issued

January 2009

Date Available

June 2014

ISSN

1431-2174

Abstract

Accurate and inexpensive identification of potentially contaminated wells is critical for water resources protection and management. The objectives of this study are to 1) assess the suitability of approximation tools such as neural networks (NN) and support vector machines (SVM) integrated in a geographic information system (GIS) for identifying contaminated wells and 2) use logistic regression and feature selection methods to identify significant variables for transporting contaminants in and through the soil profile to the groundwater. Fourteen GIS derived soil hydrogeologic and landuse parameters were used as initial inputs in this study. Well water quality data (nitrate-N) from 6,917 wells provided by Florida Department of Environmental Protection (USA) were used as an output target class. The use of the logistic regression and feature selection methods reduced the number of input variables to nine. Receiver operating characteristics (ROC) curves were used for evaluation of these approximation tools. Results showed superior performance with the NN as compared to SVM especially on training data while testing results were comparable. Feature selection did not improve accuracy; however, it helped increase the sensitivity or true positive rate (TPR). Thus, a higher TPR was obtainable with fewer variables.

Comments

Abstract only. Full-text article is available only through licensed access provided by the publisher. Published in Hydrogeology Journal, 17(6), 1507-1520. DOI: 10.1007/s10040-009-0451-1 Members of the USF System may access the full-text of the article through the authenticated link provided.

Language

en_US

Publisher

Springer

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.