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Time Series Chlorophyll-A Concentration Data Analysis: A Novel Forecasting Model for Aquaculture Industry^{ †}

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## Abstract

**:**

## 1. Introduction

## 2. Data Source

#### 2.1. The Study Area Description and Datasets Analysis

#### 2.2. Data Pre-Treatment, Filling and Correction

**Definition**

**1.**

## 3. Proposed Model

#### 3.1. Deep Learning LSTM Neural Networks

- (a)
- Forget gate equation:

- (b)
- Input gate equations:

- (c)
- Output gate equations:

- (d)
- Cell state equation:

#### 3.2. Proposed Water Quality Prediction Model

_{1,1}, LSTM

_{1,2}, …, LSTM

_{m,1}, up to LSTM

_{m,n}) in Figure 3, individual hidden layers of the stacked DL LSTM are equipped with multiple memory cells which earn the proposed prediction model the name ‘deep learning’ NN [17].

## 4. Performance Evaluation

## 5. Results and Discussions

## 6. Conclusions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**(

**a**) Installation site of the TriLux multiparameter fluorometer at the salmon aquafarm, with the inset image depicting the larger part of the salmon cage; (

**b**) Chelsea Technologies’ TriLux multiparameter fluorometer which monitors three key algal parameters in a single probe [15].

**Figure 2.**(

**a**,

**b**): Typical schematic diagram of (

**a**) Traditional RNN node, and (

**b**) Chained LSTM blocks.

**Figure 4.**(

**a,b**)

**.**Chlorophyll-a (470) dataset decomposition through the EEMD method showing (

**a**) 1 to 3 of the resultant 7 IMFs, and (

**b**) 4 to 7 of the resultant 7 IMFs.

**Figure 5.**(

**a**,

**b**). Performance comparison of real Chlorophyll-a (470) parameter values and the predicted values: (

**a**) half-day (6 h), and (

**b**) one day (24 h) prediction results.

**Table 1.**Chelsea Technology Group Fluorometers/sensors and the parameters they measure [6].

Fluorometers | Active Fluorometers | Optical Sensors | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

UniLux | TriLux | UviLux | VLux AlgaePro | VLux TPro | VLux FuelPro | VLux OilPro | LabSTAF | FastOcean APD | FastOcean | Act2 Lab | FastBallast | PAR Sensor | GlowTracka | UniLux Turbidity | ||

Fluorometers | Chlorophyll-a | |||||||||||||||

Phycobiliproteins | ||||||||||||||||

Fluorescein | ||||||||||||||||

Rhodamine | ||||||||||||||||

BTEX | ||||||||||||||||

PAH | ||||||||||||||||

Tryptophan | ||||||||||||||||

CDOM | ||||||||||||||||

Active Fluorometers | Variable Fluorescence | |||||||||||||||

Fluorescence Light Curves (FLC) | ||||||||||||||||

Phytoplankton Primary Productivity | ||||||||||||||||

Phytoplankton Cell Counting | ||||||||||||||||

Optical Sensors | PAR | |||||||||||||||

Bioluminescence | ||||||||||||||||

Turbidity | ||||||||||||||||

Absorbance |

Error Statistics | 6 Hour Prediction | 24 Hour Prediction |
---|---|---|

MSE | 0.0013 | 0.0019 |

MAE | 0.0277 | 0.0337 |

RMSE | 0.0356 | 0.0417 |

MAPE | 0.0070 | 0.0076 |

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**MDPI and ACS Style**

Eze, E.; Kirby, S.; Attridge, J.; Ajmal, T.
Time Series Chlorophyll-A Concentration Data Analysis: A Novel Forecasting Model for Aquaculture Industry. *Eng. Proc.* **2021**, *5*, 27.
https://doi.org/10.3390/engproc2021005027

**AMA Style**

Eze E, Kirby S, Attridge J, Ajmal T.
Time Series Chlorophyll-A Concentration Data Analysis: A Novel Forecasting Model for Aquaculture Industry. *Engineering Proceedings*. 2021; 5(1):27.
https://doi.org/10.3390/engproc2021005027

**Chicago/Turabian Style**

Eze, Elias, Sam Kirby, John Attridge, and Tahmina Ajmal.
2021. "Time Series Chlorophyll-A Concentration Data Analysis: A Novel Forecasting Model for Aquaculture Industry" *Engineering Proceedings* 5, no. 1: 27.
https://doi.org/10.3390/engproc2021005027