Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach

Version 1 : Received: 21 September 2020 / Approved: 22 September 2020 / Online: 22 September 2020 (10:02:09 CEST)

A peer-reviewed article of this Preprint also exists.

Eze, E.; Ajmal, T. Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach. Appl. Sci. 2020, 10, 7079. Eze, E.; Ajmal, T. Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach. Appl. Sci. 2020, 10, 7079.


Dissolved Oxygen (DO) concentration is a vital parameter that indicates water quality. DO short term forecasting using time series analysis on data collected from an aquaculture pond is presented here. This can provide 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 are strongly correlated with the original sensor data and integrate both into inputs for the NN. The hybrid EEMD-based LSTM forecasting model is then constructed. 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, three statistical performance indices were adopted: MAE, MSE, and RMSE. Results presented for short term (12-hour period) and long term (1-month period) give a strong indication of suitability of this method for forecasting DO values.


aquaculture water quality; dissolved oxygen (DO); forecasting; EEMD; LSTM


Computer Science and Mathematics, Information Systems

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