Wei, Yaxing and Bin Hashim, Huzaifa and Lai, Sai Hin and Chong, Kai Lun and Huang, Yuk Feng and Ahmed, Ali Najah and Sherif, Mohsen and El-Shafie, Ahmed (2024) Comparative analysis of artificial intelligence methods for streamflow forecasting. IEEE Access, 12. pp. 10865-10885. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2024.3351754.
Full text not available from this repository.Abstract
Deep learning excels at managing spatial and temporal time series with variable patterns for streamflow forecasting, but traditional machine learning algorithms may struggle with complicated data, including non-linear and multidimensional complexity. Empirical heterogeneity within watersheds and limitations inherent to each estimation methodology pose challenges in effectively measuring and appraising hydrological statistical frameworks of spatial and temporal variables. This study emphasizes streamflow forecasting in the region of Johor, a coastal state in Peninsular Malaysia, utilizing a 28-year streamflow-pattern dataset from Malaysia's Department of Irrigation and Drainage for the Johor River and its tropical rainforest environment. For this dataset, wavelet transformation significantly improves the resolution of lag noise when historical streamflow data are used as lagged input variables, producing a 6% reduction in the root-mean-square error. A comparative analysis of convolutional neural networks and artificial neural networks reveals these models' distinct behavioral patterns. Convolutional neural networks exhibit lower stochasticity than artificial neural networks when dealing with complex time series data and with data transformed into a format suitable for modeling. However, convolutional neural networks may suffer from overfitting, particularly in cases in which the structure of the time series is overly simplified. Using Bayesian neural networks, we modeled network weights and biases as probability distributions to assess aleatoric and epistemic variability, employing Markov chain Monte Carlo and bootstrap resampling techniques. This modeling allowed us to quantify uncertainty, providing confidence intervals and metrics for a robust quantitative assessment of model prediction variability.
| Item Type: | Article |
|---|---|
| Funders: | National Water and Energy Center, United Arab Emirates University, United Arab Emirates, for APC |
| Uncontrolled Keywords: | Artificial neural network; deep learning convolutional neural network; Bayesian statistic; streamflow; time series; uncertainty analysis |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 21 Jun 2024 03:03 |
| Last Modified: | 21 Jun 2024 03:03 |
| URI: | http://eprints.um.edu.my/id/eprint/44211 |
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