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Gewählte Publikation:

Brunetti, G; Stumpp, C; Simunek, J.
(2022): Balancing exploitation and exploration: A novel hybrid global-local optimization strategy for hydrological model calibration
ENVIRON MODELL SOFTW. 2022; 150, 105341 FullText FullText_BOKU

Optimization problems in hydrological modeling are frequently solved using local or global search strategies, which either maximize exploitation or exploration. Thus, the elevated performance of one strategy for one class of problems is often offset by poor performance for another class. To overcome this issue, we propose a hybrid strategy, G-CLPSO, that combines the global search characteristics of the Comprehensive Learning Particle Swarm Optimization (CLPSO) with the exploitation capability of the Marquardt-Levenberg (ML) method and implement it into the hydrological model, HYDRUS. Benchmarks involving optimizing non-separable unimodal and multimodal functions demonstrate that G-CLPSO outperforms CLPSO in terms of accuracy and convergence. Synthetic modeling scenarios involving the inverse estimation of soil hydraulic properties are used to compare the G-CLPSO against the original HYDRUS ML solver, the gradient-based algorithm PEST, and the stochastic SCE-UA strategy. Results demonstrate the superior performance of the G-CLPSO, suggesting a potential use in other environmental problems.
Autor*innen der BOKU Wien:
Brunetti Giuseppe
Stumpp Christine
BOKU Gendermonitor:

Find related publications in this database (Keywords)
Model calibration
Environmental modeling

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