A Water Quality Prediction Method Based on the Deep LSTM Network Considering Correlation in Smart Mariculture

Abstract

An accurate prediction of cage-cultured water quality is a hot topic in smart mariculture. Since the mariculturing environment is always open to its surroundings, the changes in water quality parameters are normally nonlinear, dynamic, changeable, and complex. However, traditional forecasting methods have lots of problems, such as low accuracy, poor generalization, and high time complexity. In order to solve these shortcomings, a novel water quality prediction method based on the deep LSTM (long short-term memory) learning network is proposed to predict pH and water temperature. Firstly, linear interpolation, smoothing, and moving average filtering techniques are used to repair, correct, and de-noise water quality data, respectively. Secondly, Pearson’s correlation coefficient is used to obtain the correlation priors between pH, water temperature, and other water quality parameters. Finally, a water quality prediction model based on LSTM is constructed using the preprocessed data and its correlation information. Experimental results show that, in the short-term prediction, the prediction accuracy of pH and water temperature can reach 98.56% and 98.97%, and the time cost of the predictions is 0.273 s and 0.257 s, respectively. In the long-term prediction, the prediction accuracy of pH and water temperature can reach 95.76% and 96.88%, respectively.

A Review of the Artificial Neural Network Models for Water Quality Prediction

“…Different types of RNNs are developed so that the neural networks have better memory ability (see Figure 1). LSTM, an improvement over RNN, adds a processor called "memory cell state" to its hidden layer to determine whether the information is useful or not [66], and this is also suitable for SRU (Simple Recurrent Unit) [67]. Furthermore, the forget gate also determines what information should be discarded from the cell state [66].…”

“…LSTM, an improvement over RNN, adds a processor called "memory cell state" to its hidden layer to determine whether the information is useful or not [66], and this is also suitable for SRU (Simple Recurrent Unit) [67]. Furthermore, the forget gate also determines what information should be discarded from the cell state [66]. TLRN has a similar structure to MLPs, but has local recurrent connections in the hidden layer (see Figure 3), with the advantages of low noise sensitivity and adaptive storage depth [55].…”

“…Clustering methods [50] such as K-means-MLP [43] identify the data belonging to a particular class. Other data-intensive approaches include decomposition [5] and evolution-related [16] forget gate also determines what information should be discarded from the cell state [66]. TLRN has a similar structure to MLPs, but has local recurrent connections in the hidden layer (see Figure 3), with the advantages of low noise sensitivity and adaptive storage depth [55].…”

Accurate Prediction Scheme of Water Quality in Smart Mariculture With Deep Bi-S-SRU Learning Network

“…Hence, they cannot meet the ever-increasing requirements in precision agriculture. In recent years, the prediction methods based on ANN (Artificial Neural Network) and deep learning have been proposed [14], [15]. They have the advantages of good robustness, high fault tolerance and sufficient fitting of complex nonlinear relations.…”

Energy and Materials-Saving Management via Deep Learning for Wastewater Treatment Plants

“…The merge operation is independent for each depth slice, and the ratio is usually 2 * 2. The result of pooling layer will have a lower dimension, and it is not easy to generate the phenomenon of over fitting [25]. or forget part of the information for the key cell state.…”