The use of stochastic objective functions in water resource optimization problems

P. Kossieris, A. Efstratiadis, and D. Koutsoyiannis, The use of stochastic objective functions in water resource optimization problems, Facets of Uncertainty: 5th EGU Leonardo Conference – Hydrofractals 2013 – STAHY 2013, Kos Island, Greece, doi:10.13140/RG.2.2.18578.66249, European Geosciences Union, International Association of Hydrological Sciences, International Union of Geodesy and Geophysics, 2013.



The hydrological and water resource problems are characterized by the presence of multiple sources of uncertainty. The implementation of Monte Carlo simulation techniques within powerful optimization methods are required, in order to handle such uncertainties. Here we examine the combined performance of those two powerful tools to a wide range of global optimization applications, which extend from mathematical problems to hydrological calibration problems. In all cases, uncertainty is explicitly considered in terms of stochastic objective functions. In particular, we test a number of benchmark functions to assess the effectiveness and efficiency of alternative optimization techniques. Moreover, we examine two real-world calibration problems, involving a lumped rainfall-runoff models and a stochastic disaggregation model. We investigate them with different calibration criteria and under different sources of uncertainty, in order to assess not only the robustness of the derived parameters but also the predictive capacity of the models.

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Our works that reference this work:

1. I. Tsoukalas, P. Kossieris, A. Efstratiadis, and C. Makropoulos, Surrogate-enhanced evolutionary annealing simplex algorithm for effective and efficient optimization of water resources problems on a budget, Environmental Modelling and Software, 77, 122–142, doi:10.1016/j.envsoft.2015.12.008, 2016.

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