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.

Developing a Novel Water Quality Prediction Model for a South African Aquaculture Farm

“…The acceptable value of σ is typically within the range of 0.2 and 0.3 [1]. In practice, the sifting process is terminated automatically once the computed σ value lies within the acceptable range of 0.2 and 0.3.…”

“…One aspect is monitoring the fluctuations of water quality parameters, e.g., dissolved oxygen, pH, temperature, salinity, etc., that are known to adversely affect the aquaculture environment. In some cases, even slight variations above or below the normal, optimal water quality parameter conditions may lead to physiological stress on the aquatic life, which can impact their feeding, breeding and increases susceptibility to diseases [1,2].…”

Time Series Chlorophyll-A Concentration Data Analysis: A Novel Forecasting Model for Aquaculture Industry

“…The EEMD technique and deep learning LSTM NN were merged to form the chlorophylla hybrid prediction model. A detailed implementation processes of the applied EEMD technique is shown in full in [13]. The LSTM deep learning NN approach is described in full detail in Section 3.1.…”

“…However, the challenge with traditional numerical methods, LSSVR, and neural networks such as RBFNN and BPNN is the inherent weakness of the long-term dependency problem. Research has shown that deep learning long short-term memory (LSTM) neural networks can overcome the above-mentioned weakness and can provide efficient applicability and reliability for water quality parameter prediction [13,14]. Additionally, combining the ensemble empirical mode decomposition (EEMD) method with deep learning LSTM neural network has demonstrated clear advantages over traditional LSTM neural networks in terms of improved water quality parameter prediction accuracy in the aquaculture environment [13].…”

A relationship between temperature, oxygen dissolved in blood and viral infections

“…decreasing when temperature increases and may be represented with a single or a combination of exponential functions. This has been established for pure water, ponds, aquacultures, marine environments [Millero et al, 2002;Geng and Duan, 2010;Karbowiak et al, 2010;Valderrama et al, 2016;Eze and Ajmal, 2020], but also for physiological aqueous solutions of NaCl and human plasma [Christoforides et al, 1969;Christmas and Bassingthwaighte, 2017]. Christoforides et al [1969] measured the concentration of oxygen X O2 [mL(gas)/L(fluid)] dissolved in human plasma at different temperatures T (°C) in the interval from 10 to 60° C, at standard atmospheric pressure.…”