Towards a hybrid algorithm for the robust calibration of rainfall–runoff models
| Autor: | Umut Okkan, Umut Kırdemir |
|---|---|
| Přispěvatelé: | Mühendislik Fakültesi |
| Rok vydání: | 2020 |
| Předmět: |
Optimization
Atmospheric Science ANOVA Rainfall runoff Hybrid Particle Swarm Optimization 010504 meteorology & atmospheric sciences Calibration (statistics) 0207 environmental engineering Metaheuristics 02 engineering and technology Geotechnical Engineering and Engineering Geology 01 natural sciences Hybrid algorithm Hydrological Model Calibration 020701 environmental engineering Metaheuristic 0105 earth and related environmental sciences Civil and Structural Engineering Water Science and Technology Mathematics Remote sensing |
| Zdroj: | Journal of Hydroinformatics. 22:876-899 |
| ISSN: | 1465-1734 1464-7141 |
| DOI: | 10.2166/hydro.2020.016 |
| Popis: | In this study, the hybrid particle swarm optimization (HPSO) algorithm was proposed and practised for the calibration of two conceptual rainfall-runoff models (dynamic water balance modelandabcde). The performance of the developed method was compared with those of several metaheuristics. The models were calibrated for three sub-basins, and multiple performance criteria were taken into consideration in comparison. The results indicated that HPSO was derived significantly better and more consistent results than other algorithms with respect to hydrological model errors and convergence speed. A variance decomposition-based method - analysis of variance (ANOVA) - was also used to quantify the dynamic sensitivity of HPSO parameters. Accordingly, the individual and interactive uncertainties of the parameters defined in the HPSO are relatively low. Scientific research projects Department of Balikesir University BAP2017/145 |
| Databáze: | OpenAIRE |
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