# Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach
## 2 Related Literature Review  
Several models based on different prediction methods have been developed for DO concentration forecasting in aquaculture ecosystems [10; 11; 12; 13; 14; 15; 16]. [PERSON] et al. [10] applied back propagation (BP) NN method, with a combination of purelin, logsig, and tansig activation functions to propose a prediction model for DO concentration in aquaculture. [PERSON] [11] proposed a forecasting model based on a smooth support vector machine (SSVM) for short-term forecasting of the aquaculture water quality. [PERSON] et al. [12] proposed a numeric forecasting model for DO status through a two-stage training for classification-driven regression (CDR). [PERSON] et al. [13] applied neural network and decision tree to conduct forecasting and warning system regarding DO concentration in carp 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. [PERSON] et al. [14] proposed a prediction model for water quality in smart marculture with deep bi-directional stacked simple recurrent unit (Bi-S-SRU) learning network. [PERSON] et al. [15] applied a deep belief network and least squares support vector regression (LSSVR) machine to propose a forecasting model based on cross-section water quality. Furthermore, [PERSON] et al. [16] used support vector regression (SVR) machine to propose a hybrid forecasting approach with genetic algorithm optimization for aquaculture ponds DO content. Although, these studies reported above have shown good performance, as demonstrated in the papers, they all share a common weakness, in the sense that they are limited to single scale feature of the dataset used for training the proposed models. In other words, each of the studies only obtain the surface features of the datasets. However, study has shown that multi-scale prediction methods can obtain more features for the forecasted signals by decomposing the original signal into several sub-sequences [17; 18]. The decomposition shows that each sub-sequence reveals the disparate intrinsic features of the original signal. The empirical mode decomposition (EMD) method is usually applied for original signal decomposition into its intrinsic multi-scale characteristics [19]. Generally, prediction methods that are based on signal's multi-scale characteristics are widely applied in different fields like short-term rainfall forecasting [20], short-term traffic flow prediction [21; 22; 23] and short-term wind power forecasting [24; 25]. In the fields of water quality forecasting in aquaculture environment, [PERSON] et al. [17] applied the ensemble empirical mode decomposition method to propose an efficient hybrid model for DO concentration forecasting in aquaculture based on original signal multi-scale features, in order to increase the forecasting accuracy of DO content [26] in the aquaculture environment. The experimental results of their proposed hybrid model established that the EEMD method is reliable and effective for the forecasting of DO concentration in intensive aquafarming. From the analysis of these related literatures, it can be seen that novel hybrid forecasting models that are based on multi-scale features of the original datasets are not only effective and reliable, but also suitable for water quality data forecasting in the field of aquaculture. Hence, our study seeks to develop a novel accurate hybrid forecasting model for DO content prediction in aquaculture environment, by combining the potentials of EEMD method with LSTM [27] neural network. Although a similar study to our proposed novel hybrid EEMD-based LSTM forecasting model was carried out by [PERSON] et al. [17] as shown above, their study applied least squares support vector regression (LSSVR), back propagation (BP) neural network, and radial basis function (RBF) neural network.