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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Vydáno v:	Water resources management Ročník 31; číslo 12; s. 3975 - 3992
Hlavní autoři:	Chadalawada, Jayashree, Havlicek, Vojtech, Babovic, Vladan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Dordrecht Springer Netherlands 01.09.2017 Springer Nature B.V
Témata:	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
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Abstract	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, motivate the use of machine learning to build hydrological models. Increasing global climate change and urbanization call for better understanding of altered rainfall-runoff processes. There is a requirement that models are intelligible estimates of underlying physics, coupling explanatory and predictive components, maintaining parsimony and accuracy. Genetic Programming, an evolutionary computation technique has been used for short-term prediction and forecast in the field of hydrology. Advancing data science in hydrology can be achieved by tapping the full potential of GP in defining an evolutionary flexible modelling framework that balances prior information, simulation accuracy and strategy for future uncertainty. As a preliminary step, GP is used in conjunction with a conceptual rainfall-runoff model to solve model configuration problem. Two datasets belonging to a tropical catchment of Singapore and a temperate catchment of South Island, New Zealand with contrasting characteristics are analyzed in this study. The results indicate that proposed approach successfully combines the merits of evolutionary algorithm and conceptual knowledge in the generation of optimal model structure and associated parameters to capture runoff dynamics of catchments.
AbstractList	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, motivate the use of machine learning to build hydrological models. Increasing global climate change and urbanization call for better understanding of altered rainfall-runoff processes. There is a requirement that models are intelligible estimates of underlying physics, coupling explanatory and predictive components, maintaining parsimony and accuracy. Genetic Programming, an evolutionary computation technique has been used for short-term prediction and forecast in the field of hydrology. Advancing data science in hydrology can be achieved by tapping the full potential of GP in defining an evolutionary flexible modelling framework that balances prior information, simulation accuracy and strategy for future uncertainty. As a preliminary step, GP is used in conjunction with a conceptual rainfall-runoff model to solve model configuration problem. Two datasets belonging to a tropical catchment of Singapore and a temperate catchment of South Island, New Zealand with contrasting characteristics are analyzed in this study. The results indicate that proposed approach successfully combines the merits of evolutionary algorithm and conceptual knowledge in the generation of optimal model structure and associated parameters to capture runoff dynamics of catchments.
Author	Babovic, Vladan Chadalawada, Jayashree Havlicek, Vojtech
Author_xml	– sequence: 1 givenname: Jayashree surname: Chadalawada fullname: Chadalawada, Jayashree email: jayashree@u.nus.edu organization: Department of Civil and Environmental Engineering, National University of Singapore – sequence: 2 givenname: Vojtech surname: Havlicek fullname: Havlicek, Vojtech organization: Faculty of Environmental Sciences, Czech University of Life Sciences Prague – sequence: 3 givenname: Vladan surname: Babovic fullname: Babovic, Vladan organization: Department of Civil and Environmental Engineering, National University of Singapore
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Copyright	Springer Science+Business Media Dordrecht 2017 Water Resources Management is a copyright of Springer, (2017). All Rights Reserved.
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SubjectTerms	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
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Title	A Genetic Programming Approach to System Identification of Rainfall-Runoff Models
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