scholarly journals Water Quality Prediction Model of a Water Diversion Project Based on the Improved Artificial Bee Colony–Backpropagation Neural Network

Water ◽  
2018 ◽  
Vol 10 (6) ◽  
pp. 806 ◽  
Author(s):  
Siyu Chen ◽  
Guohua Fang ◽  
Xianfeng Huang ◽  
Yuhong Zhang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Prediction Model ◽  
Artificial Bee Colony ◽  
Water Diversion ◽  
Quality Prediction ◽  
Backpropagation Neural Network ◽  
Water Quality Prediction ◽  
Bee Colony ◽  
Water Diversion Project

scholarly journals Research on Danjiang Water Quality Prediction Based on Improved Artificial Bee Colony Algorithm and Optimized BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jian’qiang He ◽  
Naian Liu ◽  
Mei’lin Han ◽  
Yao Chen
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Abc Algorithm ◽  
Bee Colony

In order to ensure “a river of clear water is supplied to Beijing and Tianjin” and improve the water quality prediction accuracy of the Danjiang water source, while avoiding the local optimum and premature maturity of the artificial bee colony algorithm, an improved artificial bee colony algorithm (ABC algorithm) is proposed to optimize the Danjiang water quality prediction model of BP neural network is proposed. This method improves the local and global search capabilities of the ABC algorithm by adding adaptive local search factors and mutation factors, improves the performance of local search, and avoids local optimal conditions. The improved ABC algorithm is used to optimize the weights and thresholds of the BP neural network to establish a water quality grade prediction model. Taking the water quality monitoring data of Danjiang source (Shangzhou section) from 2015 to 2019 as the research object, it is compared with GA-BP, PSO-BP, ABC-BP, and BP models. The research results show that the improved ABC-BP algorithm has the highest prediction accuracy, faster convergence speed, stronger stability, and robustness.

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.

Water quality prediction model in open limestone systems

2020 ◽  
pp. 279-282
Author(s):  
Louis McDonald ◽  
Qingyun Sun ◽  
Jeffrey Skousen ◽  
Paul Ziemkiewicz
Keyword(s):  
Water Quality ◽  
Prediction Model ◽  
Quality Prediction ◽  
Water Quality Prediction

A Study on Water Quality Prediction Model over Midterm Considering Correlation between Water Quality and Hyrologic Factors

2015 ◽  
Vol 15 (6) ◽  
pp. 459-470
Author(s):  
Mieun Kim ◽  
Jaemoon Kim ◽  
Sungjae Ye ◽  
Jaebeom Park ◽  
Miyeon Yun ◽  
...  
Keyword(s):  
Water Quality ◽  
Prediction Model ◽  
Quality Prediction ◽  
Water Quality Prediction
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