# Study of Precipitation Forecast Based on Deep Belief Networks

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## Abstract

**:**

## 1. Introduction

## 2. Related Work

## 3. Material and Methods

#### 3.1. SVM Based on the PSO

#### 3.2. Deep Belief Network

- Step 1.
- Train the raw input, $x={h}^{(0)}$, as the first RBM layer. The first layer is its visible layer.
- Step 2.
- The hidden layer of the first RBM layer is used as the visual layer of the second RBM layer. The output of the first layer is used as the input of the second layer. This representation can be chosen as being the samples of $p({h}^{(1)}|{h}^{(0)})$ or mean activations of $p({h}^{(1)}=1|{h}^{(0)})$.
- Step 3.
- Take the transformed samples or mean activations as training examples to train the second layer as an RBM.
- Step 4.
- Repeat Step 2 and Step 3, upward of either samples or mean values each iterate.
- Step 5.
- When the training period is reached, or this satisfies the stop condition, end the iteration.

## 4. Results and Discussion

#### 4.1. Data Collection and Preprocessing

#### 4.2. Data Normalization

#### 4.3. Algorithm Validation

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**The flowchart of the support vector machine with particle swarm optimization (PSO-SVM) model.

**Figure 5.**The error curve of the pre-training model in the validation set with different sample sizes.

Province | Station No. | Station Name | Latitude | Longitude | Air Pressure Sensor Pull Height (m) | Observatory Height (m) |
---|---|---|---|---|---|---|

Jiangsu | 58238 | Nanjing | 31.56 | 118.54 | 36.4 | 35.2 |

PRS (hPa) | PRS_Sea (hPa) | WIN_D (°) | WIN_S (0.1 m/s) | TEM (°C) | RHU (%) | PRE_1h (mm) |
---|---|---|---|---|---|---|

1031.2 | 1035.8 | 89 | 2.5 | 77 | 2 | 0 |

1030.8 | 1035.4 | 113 | 2.9 | 61 | 6.4 | 0 |

1027.3 | 1031.9 | 153 | 2.1 | 49 | 8.3 | 0 |

1026.2 | 1030.8 | 122 | 2 | 55 | 7.1 | 0 |

$\vdots $ | $\vdots $ | $\vdots $ | $\vdots $ | $\vdots $ | $\vdots $ | $\vdots $ |

1027.1 | 1031.7 | 121 | 0.7 | 71 | 4.1 | 0 |

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**MDPI and ACS Style**

Du, J.; Liu, Y.; Liu, Z.
Study of Precipitation Forecast Based on Deep Belief Networks. *Algorithms* **2018**, *11*, 132.
https://doi.org/10.3390/a11090132

**AMA Style**

Du J, Liu Y, Liu Z.
Study of Precipitation Forecast Based on Deep Belief Networks. *Algorithms*. 2018; 11(9):132.
https://doi.org/10.3390/a11090132

**Chicago/Turabian Style**

Du, Jinglin, Yayun Liu, and Zhijun Liu.
2018. "Study of Precipitation Forecast Based on Deep Belief Networks" *Algorithms* 11, no. 9: 132.
https://doi.org/10.3390/a11090132