# Runoff Prediction of Irrigated Paddy Areas in Southern China Based on EEMD-LSTM Model

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## Abstract

**:**

## 1. Introduction

## 2. Methods

#### 2.1. Ensemble Empirical Mode Decomposition

- 1
- Add N groups of standard normal white noise sequences ${n}_{i}\left(t\right)$ with mean 0 to the original signal ${w}_{i}\left(t\right)$ and obtain a new signal sequence ${x}_{i}\left(t\right)$:$${x}_{i}\left(t\right)={w}_{i}\left(t\right)+{n}_{i}\left(t\right)$$
- 2
- The finite Intrinsic Mode Functions (IMFs) and a trend item (R) were obtained by using the conventional EMD method:$${x}_{i}\left(t\right)={\Sigma}_{j=1}^{n}{c}_{ij}\left(t\right)+{r}_{i}\left(t\right)$$
- 3
- The EEMD decomposition results ${c}_{j}\left(t\right)$ are obtained by calculating the average value of N groups of IMF and trend item:$${c}_{j}\left(t\right)=\frac{1}{w}{\Sigma}_{i=1}^{n}{c}_{ij}\left(t\right)$$

#### 2.2. Long Short-Term Memory Network

#### 2.3. EEMD-LSTM Multivariations Model

#### 2.4. Predictive Evaluation Index

## 3. Example Analysis

#### 3.1. Study Area

#### 3.2. Data Preprocessing

#### 3.3. Model Parameter Setting

## 4. Results and Discussion

#### 4.1. Suitability Analysis of the EEMD-LSTM Multivariations Model

#### 4.2. Peak Performance Evaluation

#### 4.3. Runoff Prediction in Rainfall and Nonrainfall Events

## 5. Discussion

## 6. Conclusions

- 1
- This study proposed a prediction model of the daily runoff in irrigated paddy areas based on the EEMD-LSTM. Meteorological factors were used as the multivariations inputs, and the relationship between meteorological factors and runoff data was learned through LSTM, which solved the problem that traditional prediction methods have difficulty predicting the daily runoff in irrigated paddy areas accurately.
- 2
- By comparing the single-variation and multivariations prediction models with input data, whether decomposed by the EEMD method or not, the results show that the EEMD-LSTM multivariations model performed better than other models in simulating and predicting the daily-scale rainfall–runoff process in the irrigated paddy areas. Among them, the EEMD-LSTM${}^{\left(3\right)}$ model had the best simulation effect, with an $NSE$ of 0.86. The results demonstrate that the prediction results of the input data decomposed by the EEMD method exhibited better statistical performance than those of the original data, and the multivariations models could better solve the problem of having too little data to predict the daily runoff.
- 3
- The EEMD-LSTM${}^{\left(2\right)}$and EEMD-LSTM${}^{\left(3\right)}$ models performed well in predicting peak flow and rainfall events. Among them, the EEMD-LSTM${}^{\left(3\right)}$ model significantly outperformed the other models in predicting nonrainfall events. It demonstrated that adding adequate meteorological factor data as the input could give the LSTM model certain constraints, making it more physically meaningful and effectively improving the prediction accuracy.

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**The location of the Yangshudang watershed of the Zhanghe Irrigation System in Hubei Province (where TL is the Tuanlin meteorological station and YSD reservoir is the Yangshudang reservoir).

**Figure 5.**Performance of runoff simulations of single–variation models, (

**a**) high–flow periods, (

**b**) low–flow periods.

**Figure 6.**Performance of runoff simulations of multi–variations models, (

**a**) high–flow periods, (

**b**) low–flow periods.

Runoff | Relative Humidity | Rainfall | Solar Radiation | Maximum Temperature | Minimum Temperature | Wind Speed | |
---|---|---|---|---|---|---|---|

total | 908 | 908 | 908 | 908 | 908 | 908 | 908 |

Mean | 0.802 | 0.864 | 4.373 | 18.442 | 30.667 | 22.049 | 1.218 |

Std | 2.029 | 0.076 | 13.16 | 7.727 | 3.872 | 3.595 | 1.02 |

min | 0 | 0.22 | 0 | 10.065 | 17.0 | 2.3 | 0 |

max | 27.57 | 0.986 | 166.6 | 32.15 | 38.10 | 31 | 6.8 |

Relative Humidity | Rainfall | Solar Radiation | Maximum Temperature | Minimum Temperature | Wind Speed | |
---|---|---|---|---|---|---|

Pearson | 0.174 ** | 0.640 ** | −0.213 ** | −0.181 ** | 0.009 | 0.012 |

Clusters d | ${\mathit{R}}^{2}$ | RMSE (m${}^{3}$/s) | Time/(s) |
---|---|---|---|

2 | 0.31749 | 2.40670 | 495.52 |

3 | 0.79224 | 1.32785 | 395.51 |

4 | 0.78666 | 1.34554 | 396.17 |

5 | 0.81387 | 1.25682 | 403.93 |

6 | 0.85564 | 1.10683 | 440.31 |

7 | 0.83689 | 1.17653 | 445.97 |

8 | 0.81234 | 1.26196 | 540.86 |

9 | 0.85301 | 1.11689 | 615.22 |

10 | 0.83122 | 1.19678 | 617.01 |

Required Documents | Preprocessing | Input Features | Output Labels | |
---|---|---|---|---|

LSTM${}^{\left(1\right)}$ | Historical runoff | None | runoff | runoff |

LSTM${}^{\left(2\right)}$ | Historical rainfall, runoff | None | rainfall | runoff |

EEMD-LSTM${}^{\left(1\right)}$ | Historical runoff | EEMD | IMF${}_{1}$–IMF${}_{n}$, R | IMF${}_{1}$–IMF${}_{n}$, R |

EEMD-LSTM${}^{\left(2\right)}$ | Historical rainfall, runoff | EEMD, K-means | ${K}_{1}$–${K}_{d}$ | runoff |

EEMD-LSTM${}^{\left(3\right)}$ | Historical meteorological factors, runoff | EEMD, K-means | ${K}_{1}$–${K}_{d}$, meteorological factors | runoff |

${\mathit{R}}^{2}$ | NSE | RMSE (m${}^{3}$/s) | RAE | |
---|---|---|---|---|

LSTM${}^{\left(1\right)}$ | 0.171 | 0.172 | 2.652 | 0.648 |

EEMD-LSTM${}^{\left(1\right)}$ | 0.591 | 0.612 | 1.863 | 0.838 |

${\mathit{R}}^{2}$ | NSE | RMSE (m${}^{3}$/s) | RAE | |
---|---|---|---|---|

LSTM${}^{\left(2\right)}$ | 0.388 | 0.396 | 2.278 | 0.596 |

EEMD-LSTM${}^{\left(2\right)}$ | 0.850 | 0.853 | 1.125 | 0.355 |

EEMD-LSTM${}^{\left(3\right)}$ | 0.856 | 0.858 | 1.106 | 0.355 |

${\mathit{R}}^{2}$ | NSE | RMSE (m${}^{3}$/s) | RAE | |
---|---|---|---|---|

EEMD-LSTM${}^{\left(1\right)}$ | 0.832 | 0.832 | 1.763 | 0.455 |

EEMD-LSTM${}^{\left(2\right)}$ | 0.883 | 0.887 | 1.466 | 0.336 |

EEMD-LSTM${}^{\left(3\right)}$ | 0.881 | 0.884 | 1.485 | 0.332 |

${\mathit{R}}^{2}$ | NSE | RMSE (m${}^{3}$/s) | RAE | |
---|---|---|---|---|

EEMD-LSTM${}^{\left(1\right)}$ | 0.641 | 0.641 | 2.727 | 0.838 |

EEMD-LSTM${}^{\left(2\right)}$ | 0.879 | 0.881 | 1.580 | 0.355 |

EEMD-LSTM${}^{\left(3\right)}$ | 0.874 | 0.876 | 1.618 | 0.354 |

${\mathit{R}}^{2}$ | NSE | RMSE (m${}^{3}$/s) | RAE | |
---|---|---|---|---|

EEMD-LSTM${}^{\left(1\right)}$ | −0.039 | 0.380 | 1.208 | 1.478 |

EEMD-LSTM${}^{\left(2\right)}$ | 0.542 | 0.555 | 0.801 | 0.450 |

EEMD-LSTM${}^{\left(3\right)}$ | 0.612 | 0.623 | 0.737 | 0.437 |

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## Share and Cite

**MDPI and ACS Style**

Huang, S.; Yu, L.; Luo, W.; Pan, H.; Li, Y.; Zou, Z.; Wang, W.; Chen, J.
Runoff Prediction of Irrigated Paddy Areas in Southern China Based on EEMD-LSTM Model. *Water* **2023**, *15*, 1704.
https://doi.org/10.3390/w15091704

**AMA Style**

Huang S, Yu L, Luo W, Pan H, Li Y, Zou Z, Wang W, Chen J.
Runoff Prediction of Irrigated Paddy Areas in Southern China Based on EEMD-LSTM Model. *Water*. 2023; 15(9):1704.
https://doi.org/10.3390/w15091704

**Chicago/Turabian Style**

Huang, Shaozhe, Lei Yu, Wenbing Luo, Hongzhong Pan, Yalong Li, Zhike Zou, Wenjuan Wang, and Jialong Chen.
2023. "Runoff Prediction of Irrigated Paddy Areas in Southern China Based on EEMD-LSTM Model" *Water* 15, no. 9: 1704.
https://doi.org/10.3390/w15091704