Uncertainty analysis of monthly river flow modeling in consecutive hydrometric stations using integrated data-driven models
K Amininia, SM Saghebian - Journal of Hydroinformatics, 2021 - iwaponline.com
Journal of Hydroinformatics, 2021•iwaponline.com
HIGHLIGHTS Kernel extreme learning machine (KELM) and multivariate adaptive
regression splines (MARS) approaches were used for MRF modeling in three successive
hydrometric stations. The WT and EEMD as pre-processing methods were used for
improving the model's efficiency. Monte Carlo uncertainty analysis was applied to
investigate the dependability of the applied models.
regression splines (MARS) approaches were used for MRF modeling in three successive
hydrometric stations. The WT and EEMD as pre-processing methods were used for
improving the model's efficiency. Monte Carlo uncertainty analysis was applied to
investigate the dependability of the applied models.
HIGHLIGHTS
- Kernel extreme learning machine (KELM) and multivariate adaptive regression splines (MARS) approaches were used for MRF modeling in three successive hydrometric stations.
- The WT and EEMD as pre-processing methods were used for improving the model's efficiency.
- Monte Carlo uncertainty analysis was applied to investigate the dependability of the applied models.
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