基于三维卷积和CLSTM神经网络的水产养殖溶解氧预测

doi:10.3969/j.issn.0255-8297.2021.04.009

Abstract

Abstract: In this paper, a neural network based on three-dimensional (3D) convolution and convolutional long short-term memory (CLSTM) is proposed to predict the dissolved oxygen in aquaculture. Firstly, an input vector is multiplied by its transpose to form a single-channel matrix, and the single-channel matrices within a certain period of time are stacked to form a cube as the input data. Secondly, two consecutive three-dimensional convolutions are carried out on the input data to refine the characteristics of dissolved oxygen related factors, and the pooling layer is deleted for reducing calculation. Finally,the feature results of 3D convolution extraction are sent to the CLSTM model for further information extraction of time dimension, and the data is updated reversely by the gradient descent algorithm through the full connection layer. The actual data of a special aquaculture company in Xiangyang, Hubei Province were collected for experiment, and experimental results show that the proposed model has faster training convergence speed, higher prediction accuracy and better prediction stability than traditional BP neural network models, Conv3D and Conv2D, and could meet the needs of actual production.

Key words: three-dimensional convolutional neural network, convolutional long short-term memory (CLSTM), aquaculture, dissolved oxygen

CLC Number:

TP399

ZHA Yukun, ZHANG Qilin, ZHAO Yongbiao, HANG Bo. Prediction of Dissolved Oxygen in Aquaculture Based on 3D Convolution and CLSTM Neural Network[J]. Journal of Applied Sciences, 2021, 39(4): 615-626.

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Prediction of Dissolved Oxygen in Aquaculture Based on 3D Convolution and CLSTM Neural Network

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