# Spatial and Temporal Pattern of Rainstorms Based on Manifold Learning Algorithm

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

- If the rainfall at a single station exceeded 10 mm in 5 min and existed in isolation, and there was no rainfall at the same station 30 min before and after the observation, it was considered an unreasonable record;
- If the rainfall at a single station exceeded 10 mm in 5 min, but the observed data of other rain-measuring stations within a range of 5 × 5 km of the station was 0, it was considered an unreasonable record;
- In the case of unreasonable records from a single station, the data were compared to the rainfall isosurface map of the period. If the data from the station were confirmed to be unreasonable, the interpolation results of surrounding stations within a range of 5 × 5 km were used to replace the unreasonable records of that station.

- Rainfall events were first identified. If the 5 min rainfall at all the stations was less than 0.1 mm over four consecutive hours, it was not considered effective rainfall. Two independent rainfall events were eliminated according to this standard.
- Rainstorm samples were screened according to the yellow rainstorm warning standards of Beijing and Shenzhen. Rainstorm events were selected for further analysis.

#### 2.1. Methods and Procedures

#### 2.2. Manifold Learning Algorithm

#### 2.3. Dynamic Cluster Analysis and Feature Extraction

## 3. Results and Discussion

#### 3.1. Results

#### 3.2. Discussions

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 6.**Temporal and spatial distribution pattern of the type I characteristics of 72 h rainfall in Shenzhen.

**Figure 8.**Temporal and spatial distribution pattern of the type II characteristics of 72 h rainfall in Shenzhen.

**Figure 10.**Temporal and spatial distribution pattern of the type III characteristics of 72 h rainfall in Shenzhen.

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**MDPI and ACS Style**

Liu, Y.; Liu, Y.; Ren, H.; Du, L.; Liu, S.; Zhang, L.; Wang, C.; Gao, Q. Spatial and Temporal Pattern of Rainstorms Based on Manifold Learning Algorithm. *Water* **2023**, *15*, 37.
https://doi.org/10.3390/w15010037

**AMA Style**

Liu Y, Liu Y, Ren H, Du L, Liu S, Zhang L, Wang C, Gao Q. Spatial and Temporal Pattern of Rainstorms Based on Manifold Learning Algorithm. *Water*. 2023; 15(1):37.
https://doi.org/10.3390/w15010037

**Chicago/Turabian Style**

Liu, Yuanyuan, Yesen Liu, Hancheng Ren, Longgang Du, Shu Liu, Li Zhang, Caiyuan Wang, and Qiang Gao. 2023. "Spatial and Temporal Pattern of Rainstorms Based on Manifold Learning Algorithm" *Water* 15, no. 1: 37.
https://doi.org/10.3390/w15010037