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This work investigates the inter-relationships among stream water quality indicators, hydroclimatic variables (e.g., precipitation, river discharge), and land characteristics (e.g., soil type, land use), which is crucial to developing effective methods for water quality protection. The potential of using statistical tools, such as Principal Component (PC) and Granger causality analyses, for this purpose is assessed across 10 watersheds in the Eastern United States. The PC analysis shows consistency across the ten locations, with most of the variation explained by the first two PCs, except for the least developed watershed that presents three PCs. Results show that stronger Granger causality relationships and correlation coefficients are identified when considering a lag of one day, compared to longer lags. This is mainly due to the watersheds’ limited size and, thus, their fast hydrological response. The strongest Granger causalities are observed when water temperature and dissolved oxygen concentration are considered as the effect of the other variables, which corroborates the importance of these two water properties. This work also demonstrates how watershed size and land use can impact causalities between hydrometeorological variables and water quality, thus, highlighting how complex these relationships are even in a region characterized by overall similar climatology.
Maryam Zavareh; Viviana Maggioni; Vadim Sokolov. Investigating Water Quality Data Using Principal Component Analysis and Granger Causality. Water 2021, 13, 343 .
AMA StyleMaryam Zavareh, Viviana Maggioni, Vadim Sokolov. Investigating Water Quality Data Using Principal Component Analysis and Granger Causality. Water. 2021; 13 (3):343.
Chicago/Turabian StyleMaryam Zavareh; Viviana Maggioni; Vadim Sokolov. 2021. "Investigating Water Quality Data Using Principal Component Analysis and Granger Causality." Water 13, no. 3: 343.
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.
Maryam Zavareh; Viviana Maggioni. Application of Rough Set Theory to Water Quality Analysis: A Case Study. Data 2018, 3, 50 .
AMA StyleMaryam Zavareh, Viviana Maggioni. Application of Rough Set Theory to Water Quality Analysis: A Case Study. Data. 2018; 3 (4):50.
Chicago/Turabian StyleMaryam Zavareh; Viviana Maggioni. 2018. "Application of Rough Set Theory to Water Quality Analysis: A Case Study." Data 3, no. 4: 50.