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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 3 Authors
- Water resources management 2016 v.30 no.13 pp. 4483-4499
- algorithms; hydrologic models; rivers; runoff
- ... The calibration and selection of conceptual hydrological model parameters is an important but complex task in runoff forecasting. In order to solve the calibration of conceptual hydrological model parameters, a multi-objective cultural self-adaptive electromagnetism-like mechanism algorithm (MOCSEM) is proposed in this paper. The multi-objective parameter calibration method of runoff forecasting a ...