Water Quality Prediction Model Combining Sparse Auto-encoder and LSTM Network

Abstract

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.