Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach

Abstract

Dissolved oxygen (DO) concentration is a vital parameter that indicates water quality. We present here DO short term forecasting using time series analysis on data collected from an aquaculture pond. This can provide the basis of data support for an early warning system, for an improved management of the aquaculture farm. The conventional forecasting approaches are commonly characterized by low accuracy and poor generalization problems. In this article, we present a novel hybrid DO concentration forecasting method with ensemble empirical mode decomposition (EEMD)-based LSTM (long short-term memory) neural network (NN). With this method, first, the sensor data integrity is improved through linear interpolation and moving average filtering methods of data preprocessing. Next, the EEMD algorithm is applied to decompose the original sensor data into multiple intrinsic mode functions (IMFs). Finally, the feature selection is used to carefully select IMFs that strongly correlate with the original sensor data, and integrate into both inputs for the NN. The hybrid EEMD-based LSTM forecasting model is then constructed. The performance of this proposed model in training and validation sets was compared with the observed real sensor data. To obtain the exact evaluation accuracy of the forecasted results of the hybrid EEMD-based LSTM forecasting model, four statistical performance indices were adopted: mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Results are presented for the short term (12-h) and the long term (1-month) that are encouraging, indicating suitability of this technique for forecasting DO values.