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- Title
基于人工神经网络的晋祠泉水位模拟研究.
- Authors
邢立文; 崔宁博
- Abstract
Jinci Spring is the second largest karst spring in Taiyuan City. Since 1960 s, due to massive exploitation of karst water by industrial and agricultural production in Taiyuan City, the spring had been cut off in April 1994. In order to explore the change trend of spring water level under the influence of human factors, five artificial neural networks of Feed-forward-net, Elman-net, Time-delay-net, Narx-net and Cascade-net were used to combine 14 kinds of artificial neural networks. The training algorithm built the prediction model of Jinci spring water level. Based on the analysis of measured spring water level data from 2013 to 2017, the accuracy of various artificial neural network prediction models was analyzed. The results show that Elman recurrent dynamic neural network neural network can be used to predict Jinci spring water level accurately. The effect ratio of traincgb, trainrp, traincgf and traincgp is good and ideal. In addition, in order to make the prediction result of Jinci spring water level more realistic and instructive, this paper used LSTM deep learning model to forecast precipitation in the next 10 years and recursive dynamic neural network Elman-net to predict the future change of Jinci Spring water level. The result shows that Jinci spring water level in 2019 can exceed the lowest level of recovery flow 802.59 m.
- Publication
Yellow River, 2019, Vol 41, Issue 12, p69
- ISSN
1000-1379
- Publication type
Article
- DOI
10.3969/j.issn.1000-1379.2019.12.015