Prediction of Cyanobacterial Bloom Based on Contrast Divergence-Long-Short-Term Memory Network

A long-short-term memory and contrastive divergence technology, applied in forecasting, instrumentation, data processing applications, etc., can solve the problems of processing highly nonlinear systems and low accuracy of algal bloom forecasting, and achieve the effect of improving forecasting accuracy and training effect.

Active Publication Date: 2019-02-05
BEIJING NORMAL UNIVERSITY
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Problems solved by technology

[0006] The purpose of the present invention is to solve the problem that the existing water bloom prediction accuracy is not high, and the prediction of the highly nonlinear system cannot be processed only by a single factor or a single mathematical method. The present invention will compare the divergence a

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  • Prediction of Cyanobacterial Bloom Based on Contrast Divergence-Long-Short-Term Memory Network
  • Prediction of Cyanobacterial Bloom Based on Contrast Divergence-Long-Short-Term Memory Network
  • Prediction of Cyanobacterial Bloom Based on Contrast Divergence-Long-Short-Term Memory Network

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Embodiment 1

[0079] Taking the data of algae density, influencing factors total nitrogen, dissolved oxygen and water temperature in the Taihu Lake Basin of Jiangsu Province as an example, the method proposed by the present invention is used to predict cyanobacteria blooms. Taking the observation data of Taihu Lake from May 2010 to December 2011 as an example, after data screening and normalization processing, a total of 6242 algae density data samples and three influencing factors (total nitrogen, dissolved Oxygen, water temperature) samples, after filtering the representative factors and performing contrastive divergence algorithm on the influencing factors, the processed representative factors and influencing factors are divided into training samples and testing samples. In the training samples, the algae density curves before and after filtering are as follows: image 3 As shown, the curves of the data of the three influencing factors are as follows Figure 4 ~ Figure 6 shown. Depend ...

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Abstract

The invention discloses a method based on contrast divergence. A method for predicting cyanobacteria bloom based on long-term and short-term memory network belongs to the technical field of water environment prediction. The method comprises steps of firstly, establishing a CD-LSTM model, using the improved moving average filtering algorithm to smooth the cyanobacterial bloom characterization factors; Then the influence factors of cyanobacteria bloom were reconstructed by using the contrast divergence algorithm. Finally, the LSTM model is used to predict the cyanobacteria bloom. The invention combines the contrast divergence algorithm, the moving average filter algorithm and the long-short memory network model, and provides a method based on CD-LSTM, which can improve the prediction accuracy of cyanobacteria bloom, provides a new idea for cyanobacteria bloom prediction.

Description

technical field [0001] The invention relates to a water bloom prediction method, which belongs to the technical field of water environment prediction. Specifically, it is a cyanobacterial bloom prediction method based on Contrastive Divergence-Long Short Term Memory networks (CD-LSTM). Background technique [0002] With the development of social economy, the phenomenon of eutrophication of water body is becoming more and more common, which has caused many troubles to people's life. Eutrophication of water body is a natural phenomenon that occurs in fresh water and is caused by the sudden excessive proliferation of algae due to the high content of nitrogen, phosphorus, and potassium in the water body. The algae bloom is a phenomenon in which a large number of algae grow or gather in a water body and reach a certain concentration, and it is the result of the combined effects of eutrophication and specific conditions in the water body. The occurrence of algae bloom seriously ...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/26
CPCG06Q10/04G06Q50/26
Inventor 郑蕾张天瑞黄绵松谢恩张现国
Owner BEIJING NORMAL UNIVERSITY
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