This master project's objectives are: - Understanding of the main approaches commonly used for discharge forecasting. - Understanding of key concepts of machine learning and evolutionary optimization. - Learning how to interpret discharge forecasts, both deterministic and probabilistic, and evaluate their performance. - Work on the acquisition of near-real time satellite rainfall estimates and use them to perform discharge forecasts. - Address the optimization of reservoir operations based on probabilistic forecasts. - Study the limits of applicability of the methodology (e.g. nature of the series being forecasted, data requirements). - Study how the methodology can be applied to selected catchments in Southern Africa. - Compare the results and use of the proposed forecasting methodology with those obtained by Bayesian inference (Differential Evolution Adaptive Metropolis, DREAM).
Colin Neil Jones, Yingzhao Lian, Loris Di Natale, Jicheng Shi, Emilio Maddalena
Lorenza Salvatori, Manon Velasco
Jean-Paul Richard Kneib, Michaël Yannick Juillard