# Real-Time Probabilistic Flood Forecasting Using Multiple Machine Learning Methods

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## Abstract

**:**

## 1. Introduction

## 2. Probabilistic Forecasting and Machine Learning Methods

#### 2.1. Probabilistic Forecasting Method

#### 2.2. Support Vector Regression

#### 2.3. Fuzzy Inference Model

#### 2.4. Defuzzification Into a Probability Distribution

#### 2.5. k-Nearest Neighbors Method

## 3. Deterministic Forecasting

#### 3.1. Study Area and Data

^{2}. Hourly rainfall data from six gauges and hourly river stage data at Liwu station from 2012 to 2018 were collected, and 15 flood events with complete rainfall and stage data were obtained. The collected 15 flood events were divided into a calibration set with 10 events and a validation set with 5 events. Table 1 lists the flood events with information on the source event (typhoon or storm), date of occurrence, rainfall duration, peak flood stage, and total rainfall amount. Spatially averaged rainfall was calculated from six gauges using the Thiessen polygon method, and was used as the rainfall variable in the study.

#### 3.2. Deterministic Model Development and Forecasting

## 4. Probabilistic Forecasting

#### 4.1. Probabilistic Model Development

#### 4.2. Probabilistic Forecasting Results

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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Event No. | Name of Typhoon or Storm | Date | Rainfall Duration (h) | Peak Flood Stage (m) | Total Rainfall (mm) | Note |
---|---|---|---|---|---|---|

1 | Jelawat | 26 September 2012 | 96 | 1.22 | 85.2 | Calibration |

2 | Trami | 20 August 2013 | 71 | 2.14 | 196.3 | Calibration |

3 | Kongrey | 31 August 2013 | 44 | 1.69 | 138.0 | Calibration |

4 | Usagi | 20 September 2013 | 60 | 1.71 | 76.4 | Calibration |

5 | Fitow | 4 October 2013 | 67 | 1.62 | 118.5 | Calibration |

6 | Matmo | 21 July 2014 | 79 | 2.28 | 156.9 | Calibration |

7 | Storm 0809 | 09 August 2014 | 133 | 1.22 | 145.6 | Calibration |

8 | Fungwong | 20 September 2014 | 147 | 2.91 | 364.8 | Calibration |

9 | Dujuan | 27 September 2015 | 86 | 4.23 | 188.2 | Calibration |

10 | Meranti | 14 September 2016 | 72 | 1.69 | 103.9 | Calibration |

11 | Megi | 26 September 2016 | 77 | 3.39 | 158.5 | Validation |

12 | Storm 0601 | 1 June 2017 | 79 | 1.34 | 122.3 | Validation |

13 | Storm 1013 | 13 October 2017 | 73 | 4.41 | 351.3 | Validation |

14 | Maria | 10 July 2018 | 41 | 1.47 | 107.4 | Validation |

15 | Yutu | 1 November 2018 | 73 | 2.52 | 219.9 | Validation |

Lead Time | RMSE (m) | CE |
---|---|---|

1 hour | 0.07 | 0.99 |

2 hours | 0.15 | 0.97 |

3 hours | 0.25 | 0.93 |

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**MDPI and ACS Style**

Nguyen, D.T.; Chen, S.-T.
Real-Time Probabilistic Flood Forecasting Using Multiple Machine Learning Methods. *Water* **2020**, *12*, 787.
https://doi.org/10.3390/w12030787

**AMA Style**

Nguyen DT, Chen S-T.
Real-Time Probabilistic Flood Forecasting Using Multiple Machine Learning Methods. *Water*. 2020; 12(3):787.
https://doi.org/10.3390/w12030787

**Chicago/Turabian Style**

Nguyen, Dinh Ty, and Shien-Tsung Chen.
2020. "Real-Time Probabilistic Flood Forecasting Using Multiple Machine Learning Methods" *Water* 12, no. 3: 787.
https://doi.org/10.3390/w12030787