Data-Driven Parameter Prediction of Water Pumping Station
Abstract
: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
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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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 | m3/s |
RE_CUM_FLOW | Reverse cumulative flow | m3/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 |
R2 | 0.30 | 0.34 | 0.45 | 0.72 |
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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
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 StyleZhang, 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