Forecast of Water Quality along the Luanhe River Line Based on BP Neural Network

2019 3rd International Workshop on Renewable Energy and Development

能源学;生态环境科学

Lin, Hong^1 ; Kang, Yuanbo^2 ; Wang, Danyang^1 ; Lu, Zeyu^1 ; Tian, Wei^1 ; Wang, Shuqian^1

School of Water Conservancy and Hydroelectric Power, Hebei University of Engineering, Handan, Hebei

056000, China^1

Yellow River International Engineering Consulting (Henan) Co. Ltd., Zhengzhou, Henan

450000, China^2

关键词: BP neural network model;    BP neural networks;    Improved BP network;    Neural network model;    Thinking process;    Tolerance function;    Water quality evaluation;    Water quality indicators;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/267/3/032075/pdf
DOI  :  10.1088/1755-1315/267/3/032075

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

In order to understand the changes of water quality in the water, the paper uses the improved BP network of LM algorithm to learn and train the data, and implements the neural network model on the MATLAB platform, and uses the processed samples for the established BP neural network. Learning training, in order to prevent some neurons from reaching the state of supersaturation.By normalizing the data, the trained network is used to predict the water quality indicators of the Luanhe River Line. The results show that it is feasible to evaluate the water quality along the sputum by BP neural network model. The model has strong learning, association and fault tolerance functions. The analysis results and process are close to the thinking process and analysis method of the human brain, which makes the water quality evaluation. The accuracy of the results is greatly improved.