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).

Application of Artificial Neural Network to Predict the Water Quality Index of Brahmani River Basin Odisha, India

2018 ◽  
Vol 6 (8) ◽  
pp. 205
Author(s):  
Binayini Bhagat ◽  
D. P. Satapathy
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Physico Chemical ◽  
Artificial Neural

Water is one of the prime elements responsible for subsistence on the earth. The scarcity of potable water is gradually increasing with the increase in population. The surface water quality is a very crucial and sensitive issue and is also a great environmental concern worldwide. Surface water pollution by physical, chemical, radiological and biological contaminants can be considered as an epidemic at times, all over the world. The present research work aims at assessing the water quality index (WQI) in the surface water of Brahmani river basin in Odisha by monitoring five sampling locations. The surface water samples data were subjected to comprehensive physico-chemical analysis besides general parameters. The monthly water quality parameters were collected and analyzed from five selected gauging stations of Odisha during the months of January to December from 2011 to 2016. Eleven physical, chemical and biological water quality parameters viz. pH, Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Electrical Conductivity(EC), Nitrogen as nitrate (Nitrate-N), Total Coli-form Bacteria(TC), Fecal Coli-form Bacteria(FC), Chemical Oxygen Demand (COD), Nitrogen as ammonia (NH4-N), Total Alkalinity (TA) as CaCO3, Total Hardness (TH) as CaCO3 were selected for the analysis. Analysis of water quality for Brahmani River is done by Water Quality Index (WQI). Prediction of water quality index is done by using Artificial Neural Network (ANN).  It is apparent from WQI values that Talcher and Panposh recorded the water quality as moderate to poor and nearly unsuitable during the years 2011-2016 indicating water as not safe for domestic purposes and needs treatment, the WQI values of Kamalanga ranged from good to poor and the WQI values of Aul and Pottamundai ranged from good to moderate. Eleven physico-chemical parameters were involved in this analysis as input variables and water quality index as output variable. Two models were proposed to identify the most effective model in an attempt to predict the WQI.  Correlation between the parameters was carried out to find out the significant parameters affecting WQI. The ANN developed was trained and tested successfully using the available data sets and the performance of ANN models were determined by coefficient of determination (R2) and Root Mean Square Error (RMSE). Results show that ANN-1 gives the higher value of R2 in summer, monsoon and winter season (0.989, 0.976 and 0.959) and low RMSE (2.1865, 2.0768 and1.9657) as compared to that of the second model (ANN-2) which gives R2 value as 0.933, 0.945 and 0.943 and RMSE value as 2.8765, 2.5456 and 1.2745 for summer, monsoon and winter seasons respectively. Hence this study triggered the use of Artificial Neural Network to predict the Water Quality Index (WQI) rather than using the traditional WQI equation.

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.

scholarly journals Water Pollution and Its Causes in the Tuojiang River Basin, China: An Artificial Neural Network Analysis

2021 ◽  
Vol 13 (2) ◽  
pp. 792
Author(s):  
Zheng Zeng ◽  
Wei-Ge Luo ◽  
Zhe Wang ◽  
Fa-Cheng Yi
Keyword(s):  
Neural Network ◽  
Water Pollution ◽  
Water Quality ◽  
Artificial Neural Network ◽  
River Basin ◽  
Back Propagation ◽  
Oxygen Demand ◽  
Ammonia Nitrogen ◽  
National Standards ◽  
Artificial Neural

This work aimed to assess the water quality of the Tuojiang River Basin in recent years to provide a better understanding of its current pollution situation, and the potential pollution risks and causes. Water quality parameters such as dissolved oxygen (DO), ammonia–nitrogen (NH3-N), total phosphorus (TP), the permanganate index (CODMn), five-day biochemical oxygen demand (BOD5), pH, and concentrations of various heavy metals were measured in the Tuojiang River, according to the national standards of the People’s Republic of China. Samples were collected between 2012 to 2018 at 11 national monitoring sites in the Tuojiang River Basin. The overall water pollution situation was evaluated with back propagation artificial neural network (BP-ANN) analysis. The pollution causes were analyzed considering both industrial wastewater discharge in the upper reaches and the current pollution situation. We found potential risks of excessive NH3-N, TP, Cd, Hg, and Pb concentrations in the Tuojiang River Basin. Moreover, corresponding water pollution control suggestions were given.

scholarly journals Using Multilayer Perceptron Artificial Neural Network for Predicting and Modeling the Chemical Oxygen Demand of the Gamasiab River

2018 ◽  
Vol 5 (1) ◽  
pp. 15-20
Author(s):  
Mohamad Parsimehr ◽  
Kamran Shayesteh ◽  
Kazem Godini ◽  
Maryam Bayat Varkeshi
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Chemical Oxygen Demand ◽  
Correlation Coefficient ◽  
Multilayer Perceptron ◽  
Oxygen Demand ◽  
Input Parameters ◽  
Artificial Neural ◽  
The Cost

Concerns about water quality have widely increased in the last three decades; thus, water quality is now as important as its quantity. To study and model the quality of the Gamasiab River, its data, including chemical oxygen demand (COD), biological oxygen demand (BOD), dissolved oxygen (DO), total dissolved solids (TDS), total suspended solids in water, acidity, temperature, turbidity, and cations and anions were measured at four stations. Then, the correlations between these parameters and COD were measured using Pearson’s correlation coefficient and modeled by multilayer perceptron artificial neural network. In order to minimize the cost of the experiments performed and to provide the input parameters to the artificial neural network based on the correlations between the data and COD, the number of input parameters was reduced and finally, model No.3, with the Momentum training function and the TanhAxon activation function with the validation correlation coefficient of 0.97, mean absolute error of 2.88, and normalized root mean square error of 0.11 was identified as the most accurate model with the lowest cost. The results of the present study showed that the multilayer perceptron neural network has high ability in modeling the COD of the river, and those data correlated with each other have the greatest effect on the model. Moreover, the number of input parameters can be reduced in order to lower the cost of experiments while the performance of the model is not undermined.

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