array(2) { ["lab"]=> string(3) "868" ["publication"]=> string(4) "6348" } Water Quality Prediction Model Combining Sparse Auto-encoder and LSTM Network - Computer Vision Research Group（计算机视觉实验室） | LabXing

Computer Vision Research Group（计算机视觉实验室）

简介 计算机视觉与图像处理

大学 中国农业大学 / 信息与电气工程学院
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Water Quality Prediction Model Combining Sparse Auto-encoder and LSTM Network

2018
期刊 IFAC-PapersOnLine
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In order to improve the prediction accuracy of dissolved oxygen in aquaculture, a hybrid model based on sparse auto-encoder (SAE) and long-short-term memory network (LSTM) is proposed in this paper. The hidden layer data pre-trained by SAE contains deep latent features of water quality, and then input it into the LSTM to enhance the prediction accuracy. Experimental results show that SAE-LSTM surpasses LSTM through reducing MSE respectively by 23.3%, 53.6%, and 39.2% in the prediction steps of 3, 6, and 12 hours, and surpasses SAE-BPNN by 87.7%, 91.9%, and 90.0%, proving that our hybrid model is more accurate.

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