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...

Celý popis

Uloženo v:
Podrobná bibliografie
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0920-4741
1573-1650
DOI:10.1007/s11269-017-1719-1