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

Abstract

In the smart mariculture, the timely and accurate predictions of water quality can help farmers take countermeasures before the ecological environment deteriorates seriously. However, the openness of the mariculture environment makes the variation of water quality nonlinear, dynamic and complex. Traditional methods face challenges in prediction accuracy and generalization performance. To address these problems, an accurate water quality prediction scheme is proposed for pH, water temperature and dissolved oxygen. First, we construct a new huge raw data set collected in time series consisting of 23,204 groups of data. Then, the water quality parameters are preprocessed for data cleaning successively through threshold processing, mean proximity method, wavelet filter, and improved smoothing method. Next, the correlation between the water quality to be predicted and other dynamics parameters is revealed by the Pearson correlation coefficient method. Meanwhile, the data for training is weighted by the discovered correlation coefficients. Finally, by adding a backward SRU node to the training sequence, which can be integrated into the future context information, the deep Bi-S-SRU (Bi-directional Stacked Simple Recurrent Unit) learning network is proposed. After training, the prediction model can be obtained. The experimental results demonstrate that our proposed prediction method achieve higher prediction accuracy than the method based on RNN (Recurrent Neural Network) or LSTM (Long Short-Term Memory) with similar or less time computing complexity. In our experiments, the proposed method takes 12.5ms to predict data on average, and the prediction accuracy can reach 94.42% in the next 3∼8 days. INDEX TERMS Smart mariculture, precision agriculture, water quality prediction, SRU, deep learning.

Water Quality Prediction Using Artificial Intelligence Algorithms

“…Jaloree et al [ 17 ] have attempted to predict the WQ of the Narmada River with five WQ indicators using a decision tree model. Another study suggested the use of the deep Bidirectional Stacked Simple Recurrent Unit (Bi-S-SRU) [ 18 ] for the designing of a precise forecasting scheme of the WQ in smart mariculture.…”

Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach

“…Several models based on different prediction methods have been developed for DO concentration forecasting in aquaculture ecosystems [11][12][13][14][15][16][17]. Xiao et al [11] applied back propagation (BP) NN method with the combination of purelin, logsig, and tansig activation functions to propose a prediction model for DO concentration in aquaculture.…”

“…The effect of their proposed model in practical application shows that the designed system can use both neural network and decision tree methods to forecast DO concentration and conduct early warning by value forecasting and rule-based reasoning, respectively. Liu et al [15] proposed a prediction model for water quality in smart mariculture with deep Bi-directional Stacked Simple Recurrent Unit (Bi-S-SRU) learning network. Yan et al [16] applied deep belief network and least squares support vector regression (LSSVR) machine to propose a forecasting model based on cross-section water quality.…”

“…is the set of time and ℱ = ( 1 , 2 , ⋯ , ) is the set of features of the time series data. Then, at time , value of a feature can be given as a matrix = [ , ] × (see (15)).…”

Water Quality Prediction in the Luan River Based on 1-DRCNN and BiGRU Hybrid Neural Network Model

“…In recent years, GRU has achieved good application results in fields of time series data prediction such as meteorology [17], wind power [18], and pond aquaculture water [19]. To efficiently integrate relevant information in the context of time series data, Liu Juntao [20] and other scholars have successfully applied the bidirectional stacked simple recursive unit (Bi-S-SRU) to water quality prediction of marine aquaculture, demonstrating the feasibility of bidirectional neural network to predict water quality parameters. Aiming at the problem of PM2.5 air pollution prediction, Qing et al [21] adopted a new hybrid algorithm of one-dimensional convolution neural network (1-DCNN) and bi-directional gated recurrent units (BiGRU), which fully excavated the local characteristics of meteorological data from different sources.…”