Surface Water Quality Modeling by Regression Analysis and Artificial Neural Network

2018 ◽  
pp. 215-230 ◽  
Author(s):  
Kamal Uddin Ahamad ◽  
Pratik Raj ◽  
Najmul Haque Barbhuiya ◽  
Abhinay Deep
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Regression Analysis ◽  
Surface Water ◽  
Surface Water Quality ◽  
Water Quality Modeling ◽  
Quality Modeling ◽  
Artificial Neural

Analysis of Water Environmental Quality Using BP Artificial Neural Network in Weihe River Baoji Segment

2013 ◽  
Vol 401-403 ◽  
pp. 2147-2150 ◽  
Author(s):  
Heng Xing Xie
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Surface Water ◽  
Oxygen Demand ◽  
Surface Water Quality ◽  
Bp Artificial Neural Network ◽  
Weihe River ◽  
Artificial Neural ◽  
Water Environmental Quality

The BP artificial neural network model in type 7-5-5 was constructed with the surface water quality standard (GB3838-2002) and the surface water quality items such as BOD5 (5 day biochemical oxygen demand), COD (chemical oxygen demand), permanganate index, fluoride, NH3-N, TP (total phosphorus) and TN (total nitrogen), and the water environmental quality evaluation was conducted using the trained BP artificial neural network with the water contamination concentration data in 6 sections of Weihe river Baoji segment in year 2009. Results showed that the water quality were GradeIand GradeII in Lin Jia Cun section and Sheng Li Qiao section, and Grade III in the rest section (Wo Long Si Bridge, Guo Zhen Bridge, Cai Jia Po Bridge and Chang Xing Bridge).

Artificial neural network and mathematical approach for estimation of surface water quality parameters (case study: California, USA)

2021 ◽  
Vol 213 ◽  
pp. 75-83
Author(s):  
Esmail Shahid Salami ◽  
Marjan Salari ◽  
Mohamad Rastegar ◽  
Solmaz Nikbakht Sheibani ◽  
Majid Ehteshami
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Surface Water ◽  
Surface Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Mathematical Approach ◽  
Artificial Neural

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.

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