Application of Rough Set Theory to Water Quality Analysis: A Case Study
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 M...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-11-01
|
| Series: | Data |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5729/3/4/50 |
| _version_ | 1817988837744312320 |
|---|---|
| author | Maryam Zavareh Viviana Maggioni |
| author_facet | Maryam Zavareh Viviana Maggioni |
| author_sort | Maryam Zavareh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T00:38:32Z |
| format | Article |
| id | doaj.art-40baad9fa8d543ed808c6f636c8746c3 |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-04-14T00:38:32Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-40baad9fa8d543ed808c6f636c8746c32022-12-22T02:22:15ZengMDPI AGData2306-57292018-11-01345010.3390/data3040050data3040050Application of Rough Set Theory to Water Quality Analysis: A Case StudyMaryam Zavareh0Viviana Maggioni1Department of Civil, Environmental and Infrastructure Engineering, George Mason University, Fairfax, VA 22030, USADepartment of Civil, Environmental and Infrastructure Engineering, George Mason University, Fairfax, VA 22030, USAThis 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.https://www.mdpi.com/2306-5729/3/4/50rough set theorywater qualityattribute reductioncore attributerule extraction |
| spellingShingle | Maryam Zavareh Viviana Maggioni Application of Rough Set Theory to Water Quality Analysis: A Case Study Data rough set theory water quality attribute reduction core attribute rule extraction |
| title | Application of Rough Set Theory to Water Quality Analysis: A Case Study |
| title_full | Application of Rough Set Theory to Water Quality Analysis: A Case Study |
| title_fullStr | Application of Rough Set Theory to Water Quality Analysis: A Case Study |
| title_full_unstemmed | Application of Rough Set Theory to Water Quality Analysis: A Case Study |
| title_short | Application of Rough Set Theory to Water Quality Analysis: A Case Study |
| title_sort | application of rough set theory to water quality analysis a case study |
| topic | rough set theory water quality attribute reduction core attribute rule extraction |
| url | https://www.mdpi.com/2306-5729/3/4/50 |
| work_keys_str_mv | AT maryamzavareh applicationofroughsettheorytowaterqualityanalysisacasestudy AT vivianamaggioni applicationofroughsettheorytowaterqualityanalysisacasestudy |