# Data-Driven Parameter Prediction of Water Pumping Station

^{*}

## Abstract

**:**

^{2}) of 0.72 and a mean absolute error (MAE) of 19.14. The model can effectively solve the problem of water level prediction in the front and rear pools under complex pumping station conditions.

## 1. Introduction

- (1)
- A coupled CNN–LSTM deep neural network model is established for pumping station water level prediction, in which the CNN can extract the relationship between many features of the pumping station and an LSTM can also capture time series information with high prediction accuracy.
- (2)
- The self-attention mechanism (SA) is used to optimize the CNN, so that the model can better analyze the feature information contained in the vector, and further sort the feature importance. Finally, the bagging method is used to improve the accuracy and stability of the prediction results, making the model more robust.
- (3)
- Compare the model with the traditional machine learning algorithm support vector regression (SVR), a separate CNN, and a separate LSTM to prove its feasibility and superiority. Although the studies described above explored the ability of these methods to predict water level parameters, no studies compared their performance. Furthermore, to the best of the authors’ knowledge, this is the first time that a coupled CNN–LSTM model has been used to predict water level issues in pumping station projects.

## 2. Data

## 3. Methodology

#### 3.1. Forecasting Strategy

#### 3.2. Convolutional Neural Network (CNN)

#### 3.3. Long Short-Term Memory (LSTM)

#### 3.4. Self-Attention Mechanism

#### 3.5. CNN–LSTM Principle Based on Self-Attention Mechanism

#### 3.6. Bagging Strategy

## 4. Model Evaluations

## 5. Results

#### 5.1. Hyperparameter Configuration

#### 5.2. Feature Selection Results

#### 5.3. Comparison of Model Prediction Results

## 6. Discussion

^{2}. The CNN–LSTM model based on the self-attention mechanism performs relatively well in traditional time series prediction tasks. Compared with the separate LSTM network model, the MAE of the CNN–LSTM network was reduced by 6.53%, and the R

^{2}was increased by 0.27%. Although the effect of the rainy season water level prediction model still has advantages over the effects of separate CNN and LSTM models, these are not particularly significant. A large part of the reason for this is the extreme rainfall that has occurred frequently in northern China in recent years, resulting in large fluctuations in water levels. Next, the model will be improved by increasing the amount of data.

## 7. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 11.**Prediction results of four neural network models. (

**a**) SVR forecast results; (

**b**) CNN forecast results; (

**c**) LSTM forecast results; (

**d**) CNN–LSTM forecast results.

Feature Name | Description | Unit |
---|---|---|

SAMP_TIME | Samping time | s |

START_TIME | Startup time | s |

VOLTAGE | Voltage | V |

CURRENT | Current | A |

IPB_ANGLE | Inlet pump blade angle | ° |

PIPE_PRESS | Pipeline pressure | Mpa |

PUMP_FERQ | Pump frequency | Hz |

RUN_NUM | Number Of runs | 1 |

RUN_TIME | This run time | 1 |

CUM_RUN_TIME | Cumulative run time | 1 |

CUM_FLOW | Cumulative flow | m^{3}/s |

RE_CUM_FLOW | Reverse cumulative flow | m^{3}/s |

ACT_POWER | Active power | kw |

REACT_POWER | Reactive power | kw |

UNIT_VIB | Unit vibration | μm |

UNIT_SWING | Unit swing | mm |

OUT_PRESS | Outlet pressure | Mpa |

PUMP_SPEED | Pump speed | rpm |

INLET_WL | Inlet water level | m |

FORE_WL | Fore pool water level | m |

REAR_WL | Rear pool water level | m |

Model | Parameter | Details | Value |
---|---|---|---|

CNN, LSTM, CNN-LSTM | Minibatch | Batch size | 128 |

Epoch | 1000 | ||

L2 regularization | Penalty parameters | 0.01 | |

Decayed learning rate | Initial learning rate | 0.01 | |

Decay rate | 0.9 | ||

Decay steps | 15 | ||

Minimum learning rate | 1.00 × 10^{−4} | ||

Dropout | Dropout rate | 0.001 | |

SVR | Grid search | Kernel function | RBF |

C | 1 | ||

Gamma | 0.1 | ||

Cross-validation | k-fold | 5 |

Machine Learning Model | Neural Network Model | |||
---|---|---|---|---|

Indicator | SVR | CNN | LSTM | CNN-LSTM |

MAE | 31.02 | 29.37 | 25.67 | 19.14 |

R^{2} | 0.30 | 0.34 | 0.45 | 0.72 |

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**MDPI and ACS Style**

Zhang, J.; Yu, Y.; Yan, J.; Chen, J.
Data-Driven Parameter Prediction of Water Pumping Station. *Water* **2023**, *15*, 1128.
https://doi.org/10.3390/w15061128

**AMA Style**

Zhang J, Yu Y, Yan J, Chen J.
Data-Driven Parameter Prediction of Water Pumping Station. *Water*. 2023; 15(6):1128.
https://doi.org/10.3390/w15061128

**Chicago/Turabian Style**

Zhang, Jun, Yongchuan Yu, Jianzhuo Yan, and Jianhui Chen.
2023. "Data-Driven Parameter Prediction of Water Pumping Station" *Water* 15, no. 6: 1128.
https://doi.org/10.3390/w15061128