scholarly journals Prediction of Surface Water Quality by Artificial Neural Network Model Using Probabilistic Weather Forecasting

Water ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 2392
Author(s):  
Woo Suk Jung ◽  
Sung Eun Kim ◽  
Young Do Kim
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Weather Forecasting ◽  
Oxygen Demand ◽  
Meteorological Station ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Artificial Neural

We developed an artificial neural network (ANN)-based water quality prediction model and evaluated the applicability of the model using regional probability forecasts provided by the Korea Meteorological Administration as the input data of the model. The ANN-based water quality prediction model was constructed by reflecting the actual meteorological observation data and the water quality factors classified using an exploratory factor analysis (EFA) for each unit watershed in Nam River. To apply spatial refinement of meteorological factors for each unit watershed, we used the data of the Sancheong meteorological station for Namgang A and B, and the data of the Jinju meteorological station for Namgang C, D, and E. The predicted water quality variables were dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total organic carbon (TOC), total phosphorus (T-P), and suspended solids (SS). The ANN evaluation results reveal that the Namgang E unit watershed has a higher model accuracy than the other unit watersheds. Furthermore, compared with Namgang C and D, Namgang E has a high correlation with water quality due to meteorological effects. The results of this study will help establish a water quality forecasting system based on probabilistic weather forecasting in the long term.

Application of cuckoo search in water quality prediction using artificial neural network

2017 ◽  
Vol 6 (2/3) ◽  
pp. 229 ◽  
Author(s):  
Sankhadeep Chatterjee ◽  
Sarbartha Sarkar ◽  
Nilanjan Dey ◽  
Amira S. Ashour ◽  
Soumya Sen ◽  
...  
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Cuckoo Search ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Artificial Neural

Application of cuckoo search in water quality prediction using artificial neural network

2017 ◽  
Vol 6 (2/3) ◽  
pp. 229
Author(s):  
Aboul Ella Hassanien ◽  
Amira S. Ashour ◽  
Soumya Sen ◽  
Nilanjan Dey ◽  
Sankhadeep Chatterjee ◽  
...  
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Cuckoo Search ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
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).

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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