# A Neural Network-Based Hydrological Model for Very High-Resolution Forecasting Using Weather Radar Data

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## Abstract

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## 1. Introduction

## 2. Data and Method

^{2}, with a concentration time of less than 2 h, which is the time spent by the water to flow from the riverhead to its outlet.

## 3. Results and Discussion

## 4. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**Circular coverage of the Pico do Couto radar overlapped to the Bengalas watershed. The square with the gridded mesh corresponds to the cutout of the weather radar data employed in this study. The location of the river level gauge at the outlet is shown in the black triangle.

**Figure 3.**Observed and predicted time series of the outlet river level for 15 min (red) and 120 min (blue), between 29th December 2011 and 8th January 2012 (12-h accumulated volume time).

**Figure 4.**Scatter plot between predicted and observed values of river level at the outlet of the watershed for 15 (red) and 120 min (blue) of prediction antecedence using weather radar (12-h accumulated volume time).

**Figure 5.**Obtained maximum weights with the trained network using as input the accumulated 12 h for 15 (

**left heatmap**) and 120 min (

**right heatmap**) forecasts.

**Figure 6.**Scatter plots of shortest distance drainage x maximum input weights: in both cases, accumulated 12 h for 15 (

**left**) and 120 (

**right**) min forecasts, no correlation was found.

**Figure 7.**Correlation maps between weights and HAND. Obtained correlation values considering accumulated rain for 12 h for 15 (

**left**) and 120 (

**right**) min forecasts.

Hyperparameter | Value |
---|---|

Batch size | 1024 |

Loss function | MSE |

Optimizer | Adam |

Learning rate | 1e-3 |

Activation function (4 layers) | ReLU, ReLU, ReLU, Linear |

**Table 2.**Prediction performance for 15/120 min forecasts, where Acc. denotes accumulated volume of rainfall.

Input | Epochs | RMSE | NSE |
---|---|---|---|

Current (dBZ) | 119/113 | 0.105/0.103 | −0.257/−0.198 |

Current (mm/h) | 266/249 | 0.098/0.097 | 0.088/0.212 |

Acc. 1 h (mm) | 270/331 | 0.074/0.075 | 0.549/0.493 |

Acc. 2 h (mm) | 351/258 | 0.057/0.065 | 0.670/0.662 |

Acc. 6 h (mm) | 152/266 | 0.060/0.043 | 0.710/0.796 |

Acc. 12 h (mm) | 241/183 | 0.038/0.041 | 0.878/0.859 |

Acc. 24 h (mm) | 186/186 | 0.033/0.037 | 0.877/0.859 |

Acc. 48 h (mm) | 179/123 | 0.039/0.051 | 0.850/0.779 |

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## Share and Cite

**MDPI and ACS Style**

Santos, L.B.L.; Freitas, C.P.; Bacelar, L.; Soares, J.A.J.P.; Diniz, M.M.; Lima, G.R.T.; Stephany, S.
A Neural Network-Based Hydrological Model for Very High-Resolution Forecasting Using Weather Radar Data. *Eng* **2023**, *4*, 1787-1796.
https://doi.org/10.3390/eng4030101

**AMA Style**

Santos LBL, Freitas CP, Bacelar L, Soares JAJP, Diniz MM, Lima GRT, Stephany S.
A Neural Network-Based Hydrological Model for Very High-Resolution Forecasting Using Weather Radar Data. *Eng*. 2023; 4(3):1787-1796.
https://doi.org/10.3390/eng4030101

**Chicago/Turabian Style**

Santos, Leonardo B. L., Cintia P. Freitas, Luiz Bacelar, Jaqueline A. J. P. Soares, Michael M. Diniz, Glauston R. T. Lima, and Stephan Stephany.
2023. "A Neural Network-Based Hydrological Model for Very High-Resolution Forecasting Using Weather Radar Data" *Eng* 4, no. 3: 1787-1796.
https://doi.org/10.3390/eng4030101