# A Review of Recent Machine Learning Advances for Forecasting Harmful Algal Blooms and Shellfish Contamination

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## Abstract

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## 1. Impact of Harmful Algal Blooms (HABs) on Shellfish Safety

## 2. Forecasting HABs and Shellfish Biotoxin Contamination

## 3. Time-Series Forecasting Methods

#### 3.1. Autoregressive Models

#### 3.2. Support Vector Machine

#### 3.3. Random Forest

#### 3.4. Probabilistic Graphical Models

#### 3.5. Artificial Neural Networks

#### 3.5.1. Feed-Forward Neural Networks (FFNNs)

#### 3.5.2. Convolutional Neural Networks (CNNs)

#### 3.5.3. Recurrent Neural Networks (RNNs)

## 4. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

AEW | Adaptive Exponential Weighting |

ANN | Artificial Neural Network |

AR | Autoregressive |

ARIMA | Autoregressive Integrated Moving Average |

ARMA | Autoregressive Moving Average |

ASP | Amnesic Shellfish Poisoning |

BN | Bayesian Network |

CNN | Convolutional Neural Network |

DA-RNN | Dual-stage Attention-based RNN |

DBN | Deep Belief Network |

DSP | Diarrhetic Shellfish Poisoning |

FFNN | Feed-Forward Neural Network |

HAB | Harmful Algal Bloom |

HMM | Hidden Markov Model |

MA | Moving Average |

MAE | Mean Absolute Error |

ML | Machine Learning |

MLP | Multi-Layer Perceptron |

MTS | Multivariate Time Series |

MVLR | Multivariate Linear Regression |

LSTM | Long Short-Term Memory |

PSP | Paralytic Shellfish Poisoning |

RF | Random Forest |

RMSE | Root Mean Square Error |

RNN | Recurrent Neural Network |

SST | Sea Surface Temperature |

SVM | Support Vector Machine |

VAR | Vector Autoregressive |

WNN | Wavelet Neural Network |

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**Figure 1.**Schematic representation of a conceptual machine learning-based system to forecast shellfish contamination and support the decision making process.

**Table 1.**Strengths and weaknesses of time-series methods applied in studies to forecast HABs and shellfish contamination (ARIMA, Autoregressive Integrated Moving Average; VAR, Vector Autoregression; FFNN, Feed-Forward Neural Network; CNN, Convolutional Neural Network; RNN, Recurrent Neural Network; LSTM, Long Short-Term Memory).

Method | Strengths | Weaknesses | Ref. |
---|---|---|---|

Autoregressive Models | |||

ARIMA | Needs a small amount of data, is simple, fast, flexible, and adaptable to various types of time series. | Cannot model non-linear patterns in time series and is not applicable to multivariate cases. | [27,28,30] |

VAR | Applicable to multivariate time series, simple and flexible. | Is prone to overfit and cannot model non-linear patterns in time series. | [31] |

Support Vector Machine | Models non-linear data, needs a small amount of data, generalizes well, and assures a global optimal solution. | Has a high computational cost and tends to overfit when applied to high-dimensional multivariate time series. | [32,33,34] |

Random Forest | Models non-linear data, is robust and insensitive to missing data, and its outputs are easily interpretable. | Has a high computational cost and tends to overfit when applied to high-dimensional multivariate time series. | [12,13,35] |

Probabilistic Graphical Models | Easy to incorporate diverse data types and to specify relations between variables. Explicitly probabilistic results. | Depends on a correct manual modeling of the relations between variables. A good estimate of the joint probability distributions may require a large data set, especially with complex models. | [36] |

Artificial Neural Networks | |||

FFNN | Models dynamic, non-linear and noisy data, has a low computational cost, is easy to set up, self-adaptable, self-organizing, and error tolerant. | Yields instable outputs, can produce a local minimum result, has a low efficiency and slow convergence speed, the parameter tuning is difficult. | [14,16,30,34,37,38,39,40,41,42,43,44] |

CNN | Extracts important features from the data, can work with noisy data, has a small number of trainable weights and efficient training. | The receptive field size needs to be tuned carefully to use all relevant historical information, and struggles to capture long-term dependencies in the data. | [45] |

RNN, LSTM | Captures temporal dependencies over variable periods of time. | Has a high complexity and computational cost. | [46,47,48,49] |

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**MDPI and ACS Style**

Cruz, R.C.; Reis Costa, P.; Vinga, S.; Krippahl, L.; Lopes, M.B.
A Review of Recent Machine Learning Advances for Forecasting Harmful Algal Blooms and Shellfish Contamination. *J. Mar. Sci. Eng.* **2021**, *9*, 283.
https://doi.org/10.3390/jmse9030283

**AMA Style**

Cruz RC, Reis Costa P, Vinga S, Krippahl L, Lopes MB.
A Review of Recent Machine Learning Advances for Forecasting Harmful Algal Blooms and Shellfish Contamination. *Journal of Marine Science and Engineering*. 2021; 9(3):283.
https://doi.org/10.3390/jmse9030283

**Chicago/Turabian Style**

Cruz, Rafaela C., Pedro Reis Costa, Susana Vinga, Ludwig Krippahl, and Marta B. Lopes.
2021. "A Review of Recent Machine Learning Advances for Forecasting Harmful Algal Blooms and Shellfish Contamination" *Journal of Marine Science and Engineering* 9, no. 3: 283.
https://doi.org/10.3390/jmse9030283