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- Title
基于CNN-LSTM 模型的黄河水质预测研究.
- Authors
王 军; 高梓勋; 朱永明
- Abstract
Water quality prediction is the basic and prerequisite work for the management of water resources and prevention and control of water pollution, hut the research on water quality prediction in the Yellow River basin is relatively lagged behind. In order to improve the performance of the LSTM water quality prediction model mid increase its generalization ability, according to the periodic and non-linear characteristics of water quality changes, taking the dissolved oxygen concentration of the Xiaolangdi Reservoir on the Yellow River as the research object, a combination of convolutional neural network CNN and length was constructed. The CNN-LSTM prediction model of the time memory network LSTM had been verified by experiments. The model can efficiently extract water quality feature information and perform time series prediction. The prediction error is lower than that of the LSTM model. The average absolute error of the predicted value and the root mean square error arc 19.72% and 10.44% lower than that of the LSTM model respectively. The prediction of larger mid smaller values is more accurate mid it has better generalization performance.
- Publication
Yellow River, 2021, Vol 43, Issue 5, p96
- ISSN
1000-1379
- Publication type
Article
- DOI
10.3969/j.issn.1000-1379.2021.05.018