scholarly journals Integrating Lithostratigraphic Units and GIS-Analysis Techniques to Modified Surface Water Quality Index

2016 ◽  
Vol 07 (08) ◽  
pp. 1104-1112 ◽  
Author(s):  
Majed Ibrahim ◽  
Habes Al-Mashakbeh
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Surface Water Quality ◽  
Gis Analysis ◽  
Analysis Techniques ◽  
Lithostratigraphic Units ◽  
Modified Surface

scholarly journals THE ASSESSMENT OF SURFACE WATER QUALITY IN THE LEPENC RIVER BASIN USING WATER QUALITY INDEX (WQI) METHODOLOGY

2018 ◽  
Vol 11 (2) ◽  
pp. 653-660 ◽  
Author(s):  
P. S.Bytyçi1 ◽  
H. S. Çadraku ◽  
F. N. Zhushi Etemi ◽  
M. A. Ismaili ◽  
O. B. Fetoshi ◽  
...  
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
River Basin ◽  
Water Quality Index ◽  
Quality Index ◽  
Surface Water Quality

scholarly journals Application of artificial neural network (ANN) for water quality index (WQI) prediction for the river Warta, Poland

2021 ◽  
Vol 2130 (1) ◽  
pp. 012028
Author(s):  
M Kulisz ◽  
J Kujawska
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Surface Water ◽  
Water Quality Index ◽  
Physicochemical Parameters ◽  
Quality Index ◽  
Surface Water Quality ◽  
Network Modelling ◽  
Neural Network Modelling

Abstract The aim of this paper is to present the potential of using neural network modelling for the prediction of the surface water quality index (WQI). An artificial neural network modelling has been performed using the physicochemical parameters (TDS, chloride, TH, nitrate, and manganese) as an input layer to the model, and the WQI as an output layer. The physicochemical parameters have been taken from five measuring stations of the river Warta in the years 2014-2018 via the Chief Inspectorate of Environmental Protection (GIOŚ). The best results of modelling were obtained for networks with 5 neurons in the hidden layer. A high correlation coefficient (general and within subsets) 0.9792, low level of MSE in each subset (training, test, validation), as well as RMSE at a level of 0.624507639 serve as a confirmation. Additionally, the maximum percentage of an error for WQI value did not exceed 4%, which confirms a high level of conformity of real data in comparison to those obtained during prediction. The aforementioned results clearly present that the ANN models are effective for the prediction of the value of the Surface water quality index and may be regarded as adequate for application in simulation by units monitoring condition of the environment.

Evaluating the surface Water quality index fuzzy and its influence on water treatment

2019 ◽  
Vol 32 ◽  
pp. 100890 ◽  
Author(s):  
Mariângela Dutra de Oliveira ◽  
Oscar Luiz Teixeira de Rezende ◽  
Juliana Freitas Ramos de Fonseca ◽  
Marcelo Libânio
Keyword(s):  
Water Quality ◽  
Water Treatment ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Surface Water Quality

scholarly journals Assessment of coastal surface water quality for irrigation purpose

2020 ◽  
Vol 15 (4) ◽  
pp. 960-972
Author(s):  
M. F. Serder ◽  
M. S. Islam ◽  
M. R. Hasan ◽  
M. S. Yeasmin ◽  
M. G. Mostafa
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Surface Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Water Type ◽  
Post Monsoon ◽  
Irrigation Purpose

Abstract The study aimed to assess the coastal surface water quality for irrigation purposes through the analysis of the water samples of some selected estuaries, rivers, and ponds. The analysis results showed that the mean value of typical water quality parameters like electrical conductivity (EC), total dissolved solids (TDS), sodium (Na+), and chloride (Cl−) ions exceeded the permissible limit of the Department of Environment (DoE), Bangladesh 2010, and FAO, 1985 for the pre- and post-monsoon seasons. The Piper diagram indicated a Na-Cl water type, especially during the pre- and post-monsoon seasons. The water quality parameters in the areas showed a higher amount than the standard permissible limits, indicating that the quality is deteriorating. The water quality index values for domestic uses showed very poorly to unsuitable in most of the surface waters except pond water, especially during the pre- and post-monsoon periods. The surface water quality index for irrigation purpose usages was found to be high and/ or severely restricted (score: 0–55) during the pre- and post-monsoon seasons. The study observed that due to saline water intrusion, the water quality deterioration started from post-monsoon and reached its highest level during the pre-monsoon season, which gradually depreciates the water quality in coastal watersheds of Bangladesh.

Evaluating surface water quality using water quality index in Beiyun River, China

2020 ◽  
Vol 27 (28) ◽  
pp. 35449-35458
Author(s):  
Huihui Wu ◽  
Wenjie Yang ◽  
Ruihua Yao ◽  
Yue Zhao ◽  
Yunqiang Zhao ◽  
...  
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Surface Water Quality

scholarly journals Assessment of Surface Water Quality of Danube River in Terms of Usual Parameters and Correlation Analyses

2019 ◽  
Vol 70 (2) ◽  
pp. 398-406
Author(s):  
Romana Drasovean ◽  
Gabriel Murariu ◽  
Gigi Constantinescu ◽  
Adrian Circiumaru
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Pearson Correlation ◽  
Oxygen Demand ◽  
Surface Water Quality ◽  
Danube River ◽  
Index Method

In order to determine the water quality of Danube River, in the Galati area, the Water Quality Index was calculated. Water Quality Index is a useful number of overall qualities of water. Galati is a Danube port city located in south-eastern of Romania. Samples were taken from 9 places along the Danube starting with the place where the Siret flows into the Danube to the Profiland Steel Plant. Profiland Steel is a company in Galai whose main activities are: sheet and zinc strips; treatment and coating of metals. The monitoring period was one year, from November 2016 to December 2017. Every month, thirty physical - chemical parameters were investigated. In this study the assessment of surface water quality was determined on the basis of various indicators such as: potassium and calcium ions, nitrites, nitrates, total nitrogen, ammonium, chlorides, total phosphorus, sulphates, cadmium, chrome, copper, lead, iron, zinc, density, dissolved oxygen, chemical oxygen demand (CCO-Cr), biochemical oxygen demand (CBO5), electrical conductivity, the density of the conductivity, resistivity, pH, salinity, total dissolved solids. The water quality index (WQI) has been calculated by using Weighted Arithmetic Water Quality Index Method. Two types of correlations were developed: Pearson correlation matrix and Spearman correlation.

Application of Multivariate Statistical Methods and Water Quality Index for the Evaluation of Surface Water Quality in the Cunas River Basin, Peru

2021 ◽  
Vol 18 (4) ◽  
pp. 19-27
Author(s):  
Henry Dominguez Franco ◽  
María Custodio ◽  
Richard Peñaloza ◽  
Heidi De la Cruz
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Statistical Methods ◽  
River Basin ◽  
Water Quality Index ◽  
Quality Index ◽  
Surface Water Quality ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Ccme Wqi

Watershed management requires information that allows the intervention of possible sources that affect aquatic systems. Surface water quality in the Cunas river basin (Peru) was evaluated using multivariate statistical methods and the CCME-WQI water quality index. Twenty-seven sampling sites were established in the Cunas River and nine sites in the tributary river. Water samples were collected in two contrasting climatic seasons and the CCME-WQI was determined based on physicochemical and bacteriological parameters. The PCA generated three PC with a cumulative explained variation of 78.28 %. The generalised linear model showed strong significant positive relationships (p < 0.001) of E. coli with Fe, nitrate, Cu and TDS, and a strong significant negative relationship (p < 0.001) with pH. Overall, the CCME-WQI showed the water bodies in the upper reaches of the Cunas River as good water quality (87.07), in the middle reaches as favourable water quality (67.65) and in the lower reaches as poor water quality (34.86). In the tributary, the CCME-WQI showed the water bodies as having good water quality (82.34).

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