Water Quality Assessment Using Artificial Neural Network

2008 ◽  
Author(s):  
Ma Huiqun ◽  
Liu Ling
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Quality Assessment ◽  
Water Quality Assessment ◽  
Artificial Neural

scholarly journals Treatability influence of municipal sewage effluent on surface water quality assessment based on Nemerow pollution index using an artificial neural network

2021 ◽  
Vol 877 (1) ◽  
pp. 012008
Author(s):  
R Mohammed ◽  
B Al-Obaidi
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Quality Assessment ◽  
Oxygen Demand ◽  
Pollution Index ◽  
Water Quality Assessment ◽  
Artificial Neural ◽  
The Impact

Abstract Assessing water quality provides a scientific foundation for the development and management of water resources. The objective of the research is to evaluate the impact treated effluent from North Rustumiyia wastewater treatment plant (WWTP) on the quality of Diyala river. The model of the artificial neural network (ANN) and factor analysis (FA) based on Nemerow pollution index (NPI). To define important water quality parameters for North Al-Rustumiyia for the line(F2), the Nemerow Pollution Index was introduced. The most important parameters of assessment of water variation quality of wastewater were the parameter used in the model: biochemical oxygen demand (BOD), chemical oxygen demand (COD), suspension solids (SS), chloride, cl, hydrogen ion concentration, pH, sulfate, SO4-2, nitrate, NO3- and phosphate, PO4-3. Taking these criteria into account, samples of water from the sampling sites were graded as C, indicating the pollutant of the waste treatment. Then the water quality map using neural network model was based on the results of water quality assessment. The results showed that the model North Al-Rustumiyia for line F2 was more efficient and R2 was 0.965 with the impotence parameter was chloride (CL).

scholarly journals Prediction of Water Quality based on artificial neural network

2021 ◽  
Vol 1738 ◽  
pp. 012066
Author(s):  
Yingjia Wu ◽  
Rong Ling ◽  
Jixian Zhou ◽  
Mengxin Zhang ◽  
Wei Gao
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Artificial Neural
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