scholarly journals Surface water quality evaluation using multivariate methods and a new water quality index in the Indian River Lagoon, Florida

2007 ◽  
Vol 43 (8) ◽  
Author(s):  
Yun Qian ◽  
Kati White Migliaccio ◽  
Yongshan Wan ◽  
Yuncong Li
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Quality Evaluation ◽  
Indian River Lagoon ◽  
Surface Water Quality ◽  
Multivariate Methods ◽  
Water Quality Evaluation ◽  
Indian River

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.

Surface Water Quality Evaluation Based on Bayesian Network

2019 ◽  
Vol 93 (sp1) ◽  
pp. 54 ◽  
Author(s):  
Xiaohui Xie ◽  
Ying Liu ◽  
Yulan Luo ◽  
Qianying Du
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Bayesian Network ◽  
Quality Evaluation ◽  
Surface Water Quality ◽  
Water Quality Evaluation

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

Surface water quality evaluation and modeling of Ghataprabha River, Karnataka, India

2011 ◽  
Vol 184 (3) ◽  
pp. 1371-1378 ◽  
Author(s):  
B. K. Purandara ◽  
N. Varadarajan ◽  
B. Venkatesh ◽  
V. K. Choubey
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Quality Evaluation ◽  
Surface Water Quality ◽  
Water Quality Evaluation
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