Modelling Streamflow-Sediment Relationship Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @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