# Short-Term Rainfall Prediction Based on Radar Echo Using an Improved Self-Attention PredRNN Deep Learning Model

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## Abstract

**:**

## 1. Introduction

## 2. Data and Data Processing

#### 2.1. Study Area

#### 2.2. Data Cleaning

#### 2.3. Preparation of Dataset

## 3. Methodology

#### 3.1. Problem Definition

#### 3.2. Base Model

#### 3.3. Improved Self-Attention PredRNN

#### 3.4. Sampling Strategy

#### 3.5. Improved Loss Function

## 4. Results

#### 4.1. Implementation Details

#### 4.2. Evaluated Algorithm

#### 4.3. Analysis and Evaluation of Experimental Results

## 5. Conclusions and Discussions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Location of Ningxia Hui autonomous region and Köppen classification of Yinchuan. Ningxia Hui autonomous region, highlighted in green, is located in the northwest of China. Yinchuan, as the capital of Ningxia, straddles two climate zones: the warm summer Mediterranean climate (CSB) and the cool semi-arid climate (BSK).

**Figure 2.**Sample comparison before and after denoising. (

**a**) Original radar echo image. There is some noise caused by various factors in the original image. (

**b**) Denoised image. The denoised image does not contain outliers and can be used directly by the model.

**Figure 3.**(

**a**) The internal structure of the ST-LSTM. ST-LSTM originates from the basic structural unit of ConvLSTM, with the addition of the spatiotemporal memory state ${\mathcal{M}}_{t}$ as its feature. (

**b**) The propagation of the spatiotemporal memory. The blue arrows indicate that the spatiotemporal state ${\mathcal{M}}_{t}$ is propagated in a zigzag pattern throughout the network.

**Figure 4.**(

**a**) The operation principle of the self-attention mechanism. The three matrices ${K}_{x}$, ${Q}_{x}$ and ${V}_{x}$ are originated from the input $X$. We can extract the global key information of the input $X$ by performing the corresponding operations on ${K}_{x}$, ${Q}_{x}$ and ${V}_{x}$. (

**b**) The information processing flow of ISA-LSTM. The final hidden state ${\widehat{\mathcal{H}}}_{t}^{l}$ and the current moment memory state ${\mathcal{N}}_{t}^{l}$ are obtained from the previous moment’s memory state ${\mathcal{N}}_{t-1}^{l}$ and the intermediate hidden state ${\mathcal{H}}_{t}^{l}$ after being processed by the self-attention module and the corresponding gating mechanism.

**Figure 5.**Internal structure of the new spatiotemporal memory unit ISA-LSTM. The ISA module consists of two parts: the self-attention mechanism and the GRU-like update gate. Except for the ISA module, the rest of the ISA-LSTM is the same as the basic structural unit of the PredRNN-V2.

**Figure 6.**(

**a**) The overall network architecture of the original PredRNN-v2. The spatiotemporal memory ${\mathcal{M}}_{t}$ spreads in a zigzag pattern throughout the network, which enhances the network’s use of spatiotemporal information. (

**b**) The overall network architecture of ISA-PredRNN. The newly introduced long-term memory state ${\mathcal{N}}_{t}$ is one of the highlights of this network architecture, which allows the model to pay more attention to the long-term dependence than the original PredRNN-v2.

**Figure 7.**Prediction examples on the radar echo test set, in which the radar echo was strong but changed little. The ten images in the first row represent the radar echo information of the past hour, and the images in the subsequent rows represent the radar echo information of the future hour obtained by different model predictions. The time interval between adjacent images is 6 min.

**Figure 8.**Prediction examples on the radar echo test set, in which the radar echo was not as strong as that in the first one but changed greatly.

Model | CSI↑ | HSS↑ | POD↑ | FAR↓ |
---|---|---|---|---|

FC-LSTM | 0.4771 | 0.6060 | 0.5654 | 0.2476 |

TrajGRU | 0.6367 | 0.7489 | 0.7382 | 0.1805 |

ConvGRU | 0.6626 | 0.7707 | 0.7598 | 0.1637 |

ConvLSTM | 0.6625 | 0.7710 | 0.7057 | 0.1524 |

PredRNN-V2 | 0.6879 | 0.7910 | 0.7734 | 0.1404 |

ISA-PredRNN(w/o weight) | 0.6928 | 0.7951 | 0.7790 | 0.1391 |

ISA-PredRNN | 0.7001 | 0.8006 | 0.7921 | 0.1435 |

Model | CSI↑ | HSS↑ | POD↑ | FAR↓ |
---|---|---|---|---|

FC-LSTM | 0.2711 | 0.4075 | 0.3013 | 0.2716 |

TrajGRU | 0.4972 | 0.6459 | 0.5685 | 0.2057 |

ConvGRU | 0.5243 | 0.6711 | 0.5920 | 0.1807 |

ConvLSTM | 0.5280 | 0.6748 | 0.5913 | 0.1705 |

PredRNN-V2 | 0.5475 | 0.6916 | 0.6089 | 0.1588 |

ISA-PredRNN(w/o weight) | 0.5659 | 0.7079 | 0.6303 | 0.1546 |

ISA-PredRNN | 0.5812 | 0.7208 | 0.6542 | 0.1630 |

Model | CSI↑ | HSS↑ | POD↑ | FAR↓ |
---|---|---|---|---|

FC-LSTM | 0.0488 | 0.0913 | 0.0506 | 0.4117 |

TrajGRU | 0.2018 | 0.3273 | 0.2195 | 0.2922 |

ConvGRU | 0.2343 | 0.3707 | 0.2578 | 0.2758 |

ConvLSTM | 0.2290 | 0.3647 | 0.2467 | 0.2379 |

PredRNN-V2 | 0.2226 | 0.3531 | 0.2368 | 0.2075 |

ISA-PredRNN(w/o weight) | 0.2738 | 0.4217 | 0.2950 | 0.2055 |

ISA-PredRNN | 0.3052 | 0.4606 | 0.3347 | 0.2252 |

Model | Number of Layer | Number of Kernel | Kernel Size | MSE |
---|---|---|---|---|

FC-LSTM | 4 | 128-128-128-128 | 5 × 5 | 178.64 |

TrajGRU | 4 | 128-128-128-128 | 5 × 5 | 106.22 |

ConvGRU | 4 | 128-128-128-128 | 5 × 5 | 93.74 |

ConvLSTM | 4 | 128-128-128-128 | 5 × 5 | 91.95 |

PredRNN-V2 | 4 | 128-128-128-128 | 5 × 5 | 83.53 |

ISA-PredRNN(w/o weight) | 4 | 128-128-128-128 | 5 × 5 | 79.93 |

ISA-PredRNN | 4 | 128-128-128-128 | 5 × 5 | 78.27 |

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**MDPI and ACS Style**

Wu, D.; Wu, L.; Zhang, T.; Zhang, W.; Huang, J.; Wang, X.
Short-Term Rainfall Prediction Based on Radar Echo Using an Improved Self-Attention PredRNN Deep Learning Model. *Atmosphere* **2022**, *13*, 1963.
https://doi.org/10.3390/atmos13121963

**AMA Style**

Wu D, Wu L, Zhang T, Zhang W, Huang J, Wang X.
Short-Term Rainfall Prediction Based on Radar Echo Using an Improved Self-Attention PredRNN Deep Learning Model. *Atmosphere*. 2022; 13(12):1963.
https://doi.org/10.3390/atmos13121963

**Chicago/Turabian Style**

Wu, Dali, Li Wu, Tao Zhang, Wenxuan Zhang, Jianqiang Huang, and Xiaoying Wang.
2022. "Short-Term Rainfall Prediction Based on Radar Echo Using an Improved Self-Attention PredRNN Deep Learning Model" *Atmosphere* 13, no. 12: 1963.
https://doi.org/10.3390/atmos13121963