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Application of Rough Set Theory to Water Quality Analysis: A Case Study

Department of Civil, Environmental and Infrastructure Engineering, George Mason University, Fairfax, VA 22030, USA
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Received: 27 September 2018 / Revised: 29 October 2018 / Accepted: 3 November 2018 / Published: 7 November 2018
(This article belongs to the Special Issue Overcoming Data Scarcity in Earth Science)
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Abstract

This work proposes an approach to analyze water quality data that is based on rough set theory. Six major water quality indicators (temperature, pH, dissolved oxygen, turbidity, specific conductivity, and nitrate concentration) were collected at the outlet of the watershed that contains the George Mason University campus in Fairfax, VA during three years (October 2015–December 2017). Rough set theory is applied to monthly averages of the collected data to estimate one indicator (decision attribute) based on the remainder indicators and to determine what indicators (conditional attributes) are essential (core) to predict the missing indicator. The redundant attributes are identified, the importance degree of each attribute is quantified, and the certainty and coverage of any detected rule(s) is evaluated. Possible decision making rules are also assessed and the certainty coverage factor is calculated. Results show that the core water quality indicators for the Mason watershed during the study period are turbidity and specific conductivity. Particularly, if pH is chosen as a decision attribute, the importance degree of turbidity is higher than the one of conductivity. If the decision attribute is turbidity, the only indispensable attribute is specific conductivity and if specific conductivity is the decision attribute, the indispensable attribute beside turbidity is temperature. View Full-Text
Keywords: rough set theory; water quality; attribute reduction; core attribute; rule extraction rough set theory; water quality; attribute reduction; core attribute; rule extraction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zavareh, M.; Maggioni, V. Application of Rough Set Theory to Water Quality Analysis: A Case Study. Data 2018, 3, 50.

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