# Deep-Learning-Based Approach for Prediction of Algal Blooms

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Available Data and Preprocessing

#### 2.2. Deep Learning Architecture

#### 2.2.1. Restricted Boltzmann Machine

#### 2.2.2. Deep Belief Networks

#### 2.3. Prediction Model Based on Deep Learning

#### 2.4. Model Performance Criterion

## 3. Results and Discussion

## 4. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 2.**Structure of a standard an RBM [24].

**Figure 6.**Comparison of the measured value and the training fitting value with the deep learning model (Part of the fitting figure).

**Figure 7.**Predicted output of the deep learning model and the BP neural network against the measured values of phytoplankton density.

Learning Rate | RMSE |
---|---|

0.2 | 0.0611 |

0.1 | 0.0589 |

0.05 | 0.0475 |

0.01 | 0.0486 |

0.005 | 0.0462 |

0.001 | 0.0455 |

0.0005 | 0.0718 |

Model | Evaluation | RMSE | MRE |
---|---|---|---|

deep learning model | Training error | 0.0438 | 17.14% |

Test error | 0.0475 | 18.72% | |

BP neural network | Training error | 0.0452 | 18.06% |

Test error | 0.1286 | 25.93% |

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## Share and Cite

**MDPI and ACS Style**

Zhang, F.; Wang, Y.; Cao, M.; Sun, X.; Du, Z.; Liu, R.; Ye, X.
Deep-Learning-Based Approach for Prediction of Algal Blooms. *Sustainability* **2016**, *8*, 1060.
https://doi.org/10.3390/su8101060

**AMA Style**

Zhang F, Wang Y, Cao M, Sun X, Du Z, Liu R, Ye X.
Deep-Learning-Based Approach for Prediction of Algal Blooms. *Sustainability*. 2016; 8(10):1060.
https://doi.org/10.3390/su8101060

**Chicago/Turabian Style**

Zhang, Feng, Yuanyuan Wang, Minjie Cao, Xiaoxiao Sun, Zhenhong Du, Renyi Liu, and Xinyue Ye.
2016. "Deep-Learning-Based Approach for Prediction of Algal Blooms" *Sustainability* 8, no. 10: 1060.
https://doi.org/10.3390/su8101060