Modelling Streamflow-Sediment Relationship Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{Jaiyeola:2015:LENFI,

• author = "Adesoji Tunbosun Jaiyeola",
• title = "Modelling Streamflow-Sediment Relationship Using Genetic Programming",
• booktitle = "Advances in Energy and Environmental Science and Engineering",
• year = "2015",
• editor = "Aida Bulucea",
• volume = "41",
• series = "Energy, Environmental and Structural Engineering Series",
• pages = "124--129",
• address = "Michigan State University, East Lansing, MI, USA",
• month = sep # " 20-22",
• publisher = "WSEAS",
• keywords = "genetic algorithms, genetic programming, Streamflow, suspended sediment, GPdotNET, data-driven modelling",
• isbn13 = "978-1-61804-338-2",
• URL = "http://www.wseas.us/e-library/conferences/2015/Michigan/LENFI/LENFI-18.pdf",
• size = "6 pages",
• abstract = "The presence of sediment in a river or reservoir is detrimental to the operation and management of water resources because it affects the design, planning and management of any water resource. Hence it is important to accurately estimate the quantity of sediment flowing in a river or been transported into a reservoir. The process of measuring the quantity of sediment in a river manually or using automatic sampling device is labour intensive, expensive and time consuming. In this study a data-driven approach, genetic programming techniques is used to develop an explicit model that accurately captures the relationship between streamflow and suspended sediment. The accuracy of the developed models was evaluated using Root Mean Square Error (RMSE) and Determination Coefficient (R2). The results show that GP is capable of modelling streamflow sediment process accurately with R-squared value of 0.999 and RMS errors of 0.032 during the validation phase.",
• notes = "Mangosuthu University of Technology, Durban",

}

Genetic Programming entries for Adesoji Tunbosun Jaiyeola

Citations