Rationalization of Water Quality Parameters for Krishna River Basin Using Multivariate Statistical Techniques and Water Quality Index

2020 ◽  
pp. 35-43
Author(s):  
Vikas Varekar ◽  
Hasan Rameez ◽  
Aditya Nanekar
Keyword(s):  
Water Quality ◽  
River Basin ◽  
Water Quality Index ◽  
Quality Index ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical

scholarly journals Using multivariate statistical techniques to assess water quality of Nhu Y river in Thua Thien Hue province

2014 ◽  
Vol 17 (2) ◽  
pp. 50-60
Author(s):  
Ky Minh Nguyen ◽  
Lam Hoang Nguyen
Keyword(s):  
Water Quality ◽  
Correlation Analysis ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Total Variance ◽  
Quality Data ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical

The aims of this research are to assess water quality by organic and nutrient matters and identifying the environmental pressures, examine the impact of the loads to Nhu Y River, Thua Thien-Hue Province. Five stations were sampled at Nhu Y River, the research had monitoring of water quality parameters such as Temperature (Temp), Dissolved Oxygen (DO), Biological Oxygen Demand (BOD5), Chemical Oxygen Demand (COD), Nitrate (NO3-) and Phosphate (PO43-). The research used multivariate statistical techniques such as correlation analysis, principal component analysis (PCA) and cluster analysis (CA) to assess water quality. The correlation analysis shown a strong positive correlation exists between water quality parameters such as TempDO and BOD5COD (p<0.01). The PCA technique was applied to water quality data sets, which was obtained from Nhu Y River and the results show that the indices which has changed water quality. The results of the PCA using a varimax rotation technique were illustrated with two principal components (PC) and accounts for 62.207% of the overall total variance. The first PC accounted for 40.873% of the total variance, which was loaded with Temp, DO, BOD5 and COD. The second PC consists of NO3- and PO43- which accounts for 21.334% of the total variance, it can be due to the discharge of agricultural activities. Similarly, the CA has identified two major clusters involving: BOD5, COD, Temp, DO (the first cluster) and NO3-, PO43- (the second cluster).

scholarly journals Implementation of Machine Learning Methods for Monitoring and Predicting Water Quality Parameters

2020 ◽  
Vol 11 (2) ◽  
pp. 9285-9295 ◽  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
River Basin ◽  
Water Quality Index ◽  
Quality Index ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Learning Tools ◽  
Water Quality Data

The importance of good water quality for human use and consumption can never be underestimated, and its quality is determined through effective monitoring of the water quality index. Different approaches have been employed in the treatment and monitoring of water quality parameters (WQP). Presently, water quality is carried out through laboratory experiments, which requires costly reagents, skilled labor, and consumes time. Thereby making it necessary to search for an alternative method. Recently, machine learning tools have been successfully implemented in the monitoring, estimation, and predictions of river water quality index to provide an alternative solution to the limitations of laboratory analytical methods. In this study, the potentials of one of the machine learning tools (artificial neural network) were explored in the predictions and estimation of the Kelantan River basin. Water quality data collected from the 14 stations of the River basin was used for modeling and predicting (WQP). As for WQP analysis, the results obtained from this study show that the best prediction was obtained from the prediction of pH. The low kurtosis values of pH indicate that the appearance of outliers give a negative impact on the performance. As for WQP analysis for each station, we found that the WQP prediction in station 1, 2, and 3 give the good results. This is related to the available data of those stations that are more than the available data in other stations, except station 8.

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