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- Zhou, Jianzhong, et al. Show all 6 Authors
- Water resources management 2019 v.33 no.5 pp. 1785-1799
- case studies; deterministic models; heteroskedasticity; neural networks; planning; prediction; probability distribution; rivers; runoff; support vector machines; uncertainty
- ... Reliable forecasts of middle and long-term runoff can be highly valuable for water resources planning and management. The uncertainty of runoff forecasting is also essential for water resource managers. However, deterministic models only provide single prediction values without uncertainty attached. In this study, Gaussian Mixture Regression (GMR) approach is applied for probabilistic middle and l ...
- Zhou, Jianzhong, et al. Show all 4 Authors
- Environmental earth sciences 2016 v.75 no.6 pp. 531
- models; prediction; rivers; stream flow; support vector machines; time series analysis; wavelet; China; Yangtze River
- ... Machine learning models combined with time series decomposition are widely employed to estimate streamflow, yet the effect of the utilization of different decomposing methods on estimating accuracy is inadequately investigated and compared. In this paper, the main objective is to research the predictability of monthly streamflow using support vector machine model coupled with discrete wavelet tran ...