Research on the Improved Back Propagation Neural Network for the Aquaculture Water Quality Prediction

2016 ◽  
Vol 3 (4) ◽  
pp. 270-275
Author(s):  
Ding Jin-ting ◽  
◽  
Zang Ze-lin
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Aquaculture Water

scholarly journals Geographic Information System Technology Combined with Back Propagation Neural Network in Groundwater Quality Monitoring

2020 ◽  
Vol 9 (12) ◽  
pp. 736
Author(s):  
Jing Sun ◽  
Genhou Wang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Information System ◽  
Groundwater Quality ◽  
Back Propagation ◽  
Research Area ◽  
Quality Level ◽  
Quality Prediction ◽  
Gis Technology ◽  
Distribution Rules

This study was conducted to explore the distribution and changes of groundwater resources in the research area, and to promote the application of geographic information system (GIS) technology and its deep learning methods in chemical type distribution and water quality prediction of groundwater. The Shiyang River Basin in Minqin County was selected as the research object for analyzing the natural components distribution and its preliminary forecast in partial areas. With the priority control of groundwater pollutants, the concentration changes of four indicators (including the permanganate index) in different spatial distributions were analyzed based on the GIS technology, so as to provide a basis for the groundwater quality prediction. Taking the permanganate as a benchmark, this study evaluated the prediction effects of the conventional back propagation (BP) neural network (BPNN) model and the optimized BPNN based on the golden section (GBPNN) and wavelet transform (WBPNN). The algorithm proposed in this study is compared with several classic prediction algorithms for analysis. Groundwater quality level and distribution rules in the research area are evaluated with the proposed algorithm and GIS technology. The results reveal that GIS technology can characterize the spatial concentration distribution of natural indicators and analyze the chemical distribution of groundwater quality based on it. In contrast, the WBPNN has the best prediction result. Its average error of the whole process is 3.66%, and the errors corresponding to the six predicated values are all below 10%, which is dramatically better than the values of the other two models. The maximal prediction accuracy of the proposed algorithm is 97.68%, with an average accuracy of 96.12%. The prediction results on the water quality level are consistent with the actual condition, and the spatial distribution rules of the groundwater water quality can be shown clearly with the GIS technology combined with the proposed algorithm. Therefore, it is of great significance to explore the distribution and changes of regional groundwater quality, and this studywill play a critical role in determining the groundwater quality.

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.

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