Improvement of Deep Learning Models for River Water Level Prediction Using Complex Network Method
Abstract
:1. Introduction
2. Methodology and Material
2.1. Study Area
2.2. Flowchart
2.3. Data Description
2.4. DNN
2.5. LSTM
2.6. Complex Network
2.7. Evaluation of the Predictive Power
3. Results
3.1. Overall Performances of the DNN and the LSTM Models
3.2. Calculation of Centrality for the Water Level and Rainfall Stations
3.3. Development of the Water Level Prediction Model Using the Complex Network
4. Conclusions
5. Discussions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Classification (Stations) | Unit | Max | Min | Mean | Standard Deviation | Coefficient of Variation |
---|---|---|---|---|---|---|
Daolong | H (EL·m) | 385.85 | −37.86 | 32.23 | 41.72 | 1.29 |
Tanmy | H (EL·m) | 3832.00 | 3362.00 | 3439.75 | 46.28 | 0.01 |
Tanmy | R (mm) | 325.20 | 0.00 | 5.64 | 18.65 | 3.31 |
Phan Rang | R (mm) | 321.80 | 0.00 | 4.95 | 16.58 | 3.35 |
Nhaho | R (mm) | 259.00 | 0.00 | 2.56 | 11.94 | 4.67 |
Khanh Son | R (mm) | 373.40 | 0.00 | 5.90 | 18.31 | 3.10 |
Songha | R (mm) | 205.00 | 0.00 | 7.75 | 19.53 | 2.52 |
Nhiha | R (mm) | 280.40 | 0.00 | 3.63 | 13.92 | 3.83 |
Quanthe | R (mm) | 272.60 | 0.00 | 4.06 | 14.87 | 3.66 |
Hyper-Parameter | Value (Model (1)) | Value (Model (2)) | Value (Model (3)) |
---|---|---|---|
Learning rate | 0.1 | 0.1 | 0.1 |
Hidden layer | 3 | 4 | 3 |
Hidden nodes | 5 | 8 | 4 |
Dropout | 0.5 | 0.5 | 0.5 |
Epoch | 67 | 48 | 55 |
Batch size | 10 | 10 | 8 |
Optimizer | Adam | Adam | Adam |
Activation | ReLU | ReLU | ReLU |
Parameter | Values (Model (4)) | Value (Model (5)) | Values (Model (6)) |
---|---|---|---|
Activation | Relu | Relu | Relu |
Epoch | 47 | 34 | 41 |
Otimizer | Adam | Adam | Adam |
Learning rate | 0.01 | 0.01 | 0.01 |
Loss | Mean squared error | Mean squared error | Mean squared error |
Classification | CC | NSE | NRMSE (%) |
---|---|---|---|
Model (1)_DNN (rainfall) | 0.90 | 0.80 | 30.4 |
Model (2)_DNN (water level) | 0.91 | 0.89 | 16.75 |
Model (3)_DNN (water level and rainfall) | 0.92 | 0.88 | 15.22 |
Model (4)_LSTM (rainfall) | 0.94 | 0.93 | 10.41 |
Model (5)_LSTM (water level) | 0.95 | 0.95 | 9.82 |
Model (6)_LSTM (water level and rainfall) | 0.95 | 0.94 | 9.93 |
Hyper-Parameter | Values (Model (7)) | Parameter | Values (Model (8)) |
---|---|---|---|
Learning rate | 0.1 | Activation | ReLU |
Hidden layer | 3 | Epoch | 53 |
Hidden nodes | 3 | Otimizer | Adam |
Dropout | 0.5 | Learning rate | 0.01 |
Epoch | 81 | Loss | Mean squared error |
Batch size | 7 | ||
Optimizer | Adam | ||
Activation | ReLU |
Classification | CC | NSE | NRMSE (%) |
---|---|---|---|
Model (1)_DNN (rainfall) | 0.90 | 0.80 | 30.4 |
Model (2)_DNN (water level) | 0.91 | 0.89 | 16.75 |
Model (3)_DNN (water level and rainfall) | 0.92 | 0.88 | 15.22 |
Model (4)_LSTM (rainfall) | 0.94 | 0.93 | 10.41 |
Model (5)_LSTM (water level) | 0.95 | 0.95 | 9.82 |
Model (6)_LSTM (water level and rainfall) | 0.95 | 0.94 | 9.93 |
Model (7)_Complex network_DNN (water level and rainfall) | 0.95 | 0.89 | 4.41 |
Model (8)_Complex network_LSTM (water level and rainfall) | 0.99 | 0.99 | 0.17 |
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Kim, D.; Han, H.; Wang, W.; Kim, H.S. Improvement of Deep Learning Models for River Water Level Prediction Using Complex Network Method. Water 2022, 14, 466. https://doi.org/10.3390/w14030466
Kim D, Han H, Wang W, Kim HS. Improvement of Deep Learning Models for River Water Level Prediction Using Complex Network Method. Water. 2022; 14(3):466. https://doi.org/10.3390/w14030466
Chicago/Turabian StyleKim, Donghyun, Heechan Han, Wonjoon Wang, and Hung Soo Kim. 2022. "Improvement of Deep Learning Models for River Water Level Prediction Using Complex Network Method" Water 14, no. 3: 466. https://doi.org/10.3390/w14030466