Developing NARX Neural Networks for Accurate Water Level Forecasting
A reliable model for predicting fluctuations in water levels in the reservoir is essential for effective planning to manage the potential risks of flooding. A nonlinear autoregressive network with exogenous inputs (NARX) model is proposed to predict the water level of the Temengor Reservoir, Perak i...
Saved in:
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Book chapter |
| Published: |
Springer Science and Business Media Deutschland GmbH
2024
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | A reliable model for predicting fluctuations in water levels in the reservoir is essential for effective planning to manage the potential risks of flooding. A nonlinear autoregressive network with exogenous inputs (NARX) model is proposed to predict the water level of the Temengor Reservoir, Perak in Malaysia. The hyper-parameters of the proposed model have been optimized to enhance the accuracy of the proposed model while the Levenberg-Marquardt method was used to train the model. The NARX algorithm is capable of accurately predicting water levels with a high degree of accuracy. The use of the such technique for water level monitoring can be beneficial in the design of mitigation strategies for future flooding events, as it provides a critical parameter for gauging the potential severity of a flooding event. By understanding the changes in water level, emergency management teams can better prepare for and respond to floods, helping to minimize the damage and destruction they can cause. � The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. |
|---|