An Ensemble Empirical Mode Decomposition, Self-Organizing Map, and Linear Genetic Programming Approach for Forecasting River Streamflow

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{barge:2016:Water,

• author = "Jonathan T. Barge and Hatim O. Sharif",
• title = "An Ensemble Empirical Mode Decomposition, {Self-Organizing} Map, and Linear Genetic Programming Approach for Forecasting River Streamflow",
• journal = "Water",
• year = "2016",
• volume = "8",
• number = "6",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2073-4441",
• URL = "https://www.mdpi.com/2073-4441/8/6/247",
• DOI = "doi:10.3390/w8060247",
• abstract = "This study focused on employing Linear Genetic Programming (LGP), Ensemble Empirical Mode Decomposition (EEMD), and the Self-Organising Map (SOM) in modelling the rainfall-runoff relationship in a mid-size catchment. Models were assessed with regard to their ability to capture daily discharge at Lock and Dam 10 along the Kentucky River as well as the hybrid design of EEM-SOM-LGP to make predictions multiple time-steps ahead. Different model designs were implemented to demonstrate the improvements of hybrid designs compared to LGP as a standalone application. Additionally, LGP was used to gain a better understanding of the catchment in question and to assess its ability to capture different aspects of the flow hydrograph. As a standalone application, LGP was able to outperform published Artificial Neural Network (ANN) results over the same dataset, posting an average absolute relative error (AARE) of 17.118 and Nash-Sutcliff (E) of 0.937. Using EEMD derived IMF runoff subcomponents for forecasting daily discharge resulted in an AARE of 14.232 and E of 0.981. Clustering the EEMD-derived input space through an SOM before LGP application returned the strongest results, posting an AARE of 10.122 and E of 0.987. Applying LGP to the distinctive low and high flow seasons demonstrated a loss in correlation for the low flow season with an under-predictive nature signified by a normalised mean biased error (NMBE) of -2.353. Separating the rising and falling trends of the hydrograph showed that the falling trends were more easily captured with an AARE of 8.511 and E of 0.968 compared to the rising trends AARE of 38.744 and E of 0.948. Using the EEMD-SOM-LGP design to make predictions multiple-time-steps ahead resulted in a AARE of 43.365 and E of 0.902 for predicting streamflow three days ahead. The results demonstrate the effectiveness of using EEMD and an SOM in conjunction with LGP for streamflow forecasting.",
• notes = "also known as \cite{w8060247}",

}

Genetic Programming entries for Jonathan T Barge Hatim O Sharif

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