A Genetic Programming Approach to System Identification of Rainfall-Runoff Models

Advancements in data acquisition, storage and retrieval are progressing at an extraordinary rate, whereas the same in the field of knowledge extraction from data is yet to be accomplished. The challenges associated with hydrological datasets, including complexity, non-linearity and multicollinearity...

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Veröffentlicht in:	Water resources management Jg. 31; H. 12; S. 3975 - 3992
Hauptverfasser:	Chadalawada, Jayashree, Havlicek, Vojtech, Babovic, Vladan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.09.2017 Springer Nature B.V
Schlagworte:	Accuracy algorithms artificial intelligence Atmospheric Sciences Catchments Civil Engineering Climate change Climate models Computation Computer simulation Data Data acquisition data collection Datasets Dynamics Earth and Environmental Science Earth Sciences Environment Evolutionary algorithms Frameworks Genetic algorithms Geotechnical Engineering & Applied Earth Sciences Global climate Hydrogeology Hydrologic models Hydrology Hydrology/Water Resources Learning algorithms Linearity Machine learning Mathematical models Modelling New Zealand Physics prediction Predictions Rain Rainfall Rainfall-runoff relationships Runoff Singapore Storage System identification Tropical climate uncertainty Urbanization Watersheds Singapore New Zealand Rainfall-Runoff models Automatic model generation Conceptual modelling System identification Genetic programming
ISSN:	0920-4741, 1573-1650
Online-Zugang:	Volltext
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