GIS and Artificial Neural Network–Based Water Quality Model for a Stream Network in the Upper Green River Basin, Kentucky, USA

2015 ◽  
Vol 141 (5) ◽  
pp. 04014082 ◽  
Author(s):  
Jagadeesh Anmala ◽  
Ouida W. Meier ◽  
Albert J. Meier ◽  
Scott Grubbs
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
River Basin ◽  
Water Quality Model ◽  
Quality Model ◽  
Stream Network ◽  
Green River Basin ◽  
Green River ◽  
Artificial Neural

EFFECTS OF DATA NORMALIZATION ON WATER QUALITY MODEL IN A RECIRCULATORY AQUACULTURE SYSTEM USING ARTIFICIAL NEURAL NETWORK

2018 ◽  
Vol 5 (3) ◽  
pp. 21 ◽  
Author(s):  
A. FOLORUNSO TALIHA ◽  
M. AIBINU ABIODUN ◽  
G. KOLO JONATHAN ◽  
O. E. SADIKU SULEIMAN ◽  
M. ORIRE ABDULLAHI ◽  
...  
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Water Quality Model ◽  
Data Normalization ◽  
Quality Model ◽  
Aquaculture System ◽  
Artificial Neural

scholarly journals Hydropower Optimization Using Artificial Neural Network Surrogate Models of a High-Fidelity Hydrodynamics and Water Quality Model

2017 ◽  
Vol 53 (11) ◽  
pp. 9444-9461 ◽  
Author(s):  
Amelia R. Shaw ◽  
Heather Smith Sawyer ◽  
Eugene J. LeBoeuf ◽  
Mark P. McDonald ◽  
Boualem Hadjerioua
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Surrogate Models ◽  
Water Quality Model ◽  
High Fidelity ◽  
Quality Model ◽  
Artificial Neural ◽  
Hydropower Optimization

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 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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