Articles | Volume 25, issue 5
https://doi.org/10.5194/hess-25-2543-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-2543-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 May 2021
Research article |
| 18 May 2021
| 18 May 2021
Resampling and ensemble techniques for improving ANN-based high-flow forecast accuracy
Everett Snieder
Department of Civil Engineering, York University, 4700 Keele St, Toronto ON, M3J 1P3, Canada
Karen Abogadil
Department of Civil Engineering, York University, 4700 Keele St, Toronto ON, M3J 1P3, Canada
Usman T. Khan
CORRESPONDING AUTHOR
Department of Civil Engineering, York University, 4700 Keele St, Toronto ON, M3J 1P3, Canada
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- Combining Synthetic and Observed Data to Enhance Machine Learning Model Performance for Streamflow Prediction S. López-Chacón et al. 10.3390/w15112020
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- Modeling flood susceptibility zones using hybrid machine learning models of an agricultural dominant landscape of India S. Saini et al. 10.1007/s11356-023-29049-9
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- Flood susceptibility mapping using ANNs: a case study in model generalization and accuracy from Ontario, Canada R. Khalid & U. Khan 10.1080/10106049.2024.2316653
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17 citations as recorded by crossref.
- Enhancing Machine Learning Performance in Estimating CDOM Absorption Coefficient via Data Resampling J. Kim et al. 10.3390/rs16132313
- A novel long-short term memory network approach for stress model updating for excavations in high stress environments J. Morgenroth et al. 10.1080/17499518.2023.2182889
- Simulated annealing coupled with a Naïve Bayes model and base flow separation for streamflow simulation in a snow dominated basin H. Tongal & M. Booij 10.1007/s00477-022-02276-1
- Combining Synthetic and Observed Data to Enhance Machine Learning Model Performance for Streamflow Prediction S. López-Chacón et al. 10.3390/w15112020
- Enhanced hydrological drought prediction in the Gediz Basin: integrating meteorological drought via hybrid wavelet-machine learning-random oversampling models using E. Taylan 10.2166/wcc.2024.324
- SHAP-powered insights into spatiotemporal effects: Unlocking explainable Bayesian-neural-network urban flood forecasting W. Chu et al. 10.1016/j.jag.2024.103972
- Predicting River Discharge in the Niger River Basin: A Deep Learning Approach S. Ogunjo et al. 10.3390/app14010012
- WSMOTER: a novel approach for imbalanced regression L. Camacho & F. Bacao 10.1007/s10489-024-05608-6
- Influence of resampling techniques on Bayesian network performance in predicting increased algal activity M. Zeinolabedini Rezaabad et al. 10.1016/j.watres.2023.120558
- Application of Oversampling Techniques for Enhanced Transverse Dispersion Coefficient Estimation Performance Using Machine Learning Regression S. Lee & I. Park 10.3390/w16101359
- Modelling point-of-consumption residual chlorine in humanitarian response: Can cost-sensitive learning improve probabilistic forecasts? M. De Santi et al. 10.1371/journal.pwat.0000040
- EVALUATION OF APPLICABILITY OF DATA AUGMENTATION METHOD FOR DAM INFLOW PREDICTION USING DEEP LEARNING M. HITOKOTO et al. 10.2208/jscejhe.78.2_I_175
- Modeling flood susceptibility zones using hybrid machine learning models of an agricultural dominant landscape of India S. Saini et al. 10.1007/s11356-023-29049-9
- APPLICATION OF DEEP LEARNING TO DAM INFLOW FORECASTS WITH DIFFERENT CHARACTERISTICS IN THE CHUBU AREA AND THE EFFECT OF FORECAST ACCURACY CAUSED BY MIX OF INPUT RAINFALL TYPES T. KUREBAYASHI et al. 10.2208/jscejj.23-16182
- Monthly streamflow forecasting for the Hunza River Basin using machine learning techniques S. Khan et al. 10.2166/wpt.2023.124
- Flood susceptibility mapping using ANNs: a case study in model generalization and accuracy from Ontario, Canada R. Khalid & U. Khan 10.1080/10106049.2024.2316653
- EVALUATION OF THE APPLICABILITY OF DATA AUGMENTATION FOR DAM INFLOW PREDICTION USING DEEP LEARNING M. HITOKOTO et al. 10.2208/journalofjsce.24-00029
1 citations as recorded by crossref.
Latest update: 21 Nov 2024
Short summary
Flow distributions are highly skewed, resulting in low prediction accuracy of high flows when using artificial neural networks for flood forecasting. We investigate the use of resampling and ensemble techniques to address the problem of skewed datasets to improve high flow prediction. The methods are implemented both independently and in combined, hybrid techniques. This research presents the first analysis of the effects of combining these methods on high flow prediction accuracy.
Flow distributions are highly skewed, resulting in low prediction accuracy of high flows when...
