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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 5 Authors
- Stochastic environmental research and risk assessment 2014 v.28 no.7 pp. 1755-1767
- entropy; hydrologic models; neural networks; probability distribution; rivers; stream flow; China
- ... The rainfall–runoff relationship is not only nonlinear and complex but also difficult to model. Artificial neural network (ANN), as a data-driven technique, has gained significant attention in recent years and has been shown to be an efficient alternative to traditional methods for hydrological modeling. However, for different input combinations, ANN models can yield different results. Therefore, ...