eprintid: 1627
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/16/27
datestamp: 2023-11-09 15:49:47
lastmod: 2023-11-09 15:49:47
status_changed: 2023-11-09 15:41:01
type: conference_item
metadata_visibility: show
creators_name: Mustafa, M.R.U.
creators_name: Isa, M.H.
creators_name: Bhuiyan, R.R.
title: Prediction of river suspended sediment load using radial basis function neural network-a case study in Malaysia
ispublished: pub
keywords: ANN; Coefficient of determination; Engineering project; Flowing waters; Gaussian functions; Malaysia; Nonlinear behavior; Performance evaluation criteria; Radial basis function neural networks; Radial basis functions; RBF model; RBF Neural Network; River suspended sediments; Root mean square errors; Sediment discharge; Statistical performance; Time-series data; Water discharges, Anoxic sediments; Discharge (fluid mechanics); Forecasting; Mean square error; Models; Neural networks; Rivers; Sedimentology; Sediments; Suspended sediments; Sustainable development; Water resources, Radial basis function networks
note: cited By 6; Conference of 3rd National Postgraduate Conference - Energy and Sustainability: Exploring the Innovative Minds, NPC 2011 ; Conference Date: 19 September 2011 Through 20 September 2011; Conference Code:88531
abstract: Rivers contain a large amount of sediment along with flowing water. It is vital to know the sediment discharge in a river while designing different water resources engineering projects. In this study, suspended sediment discharge has been predicted using a radial basis function (RBF) neural network. Time series data of water discharge and suspended sediment discharge of Pari River, in Perak, Malaysia has been used for modeling the network. The most common radial basis function, called the Gaussian function has been used for modeling the RBF neural network. Three different statistical performance measures namely the root mean square error (RMSE), coefficient of determination (R 2) and coefficient of efficiency (CE) were used as performance evaluation criterion for the model. Results obtained from the RBF model are satisfactory and was found that RBF is able to predict the nonlinear behavior of suspended sediment discharge of Pari River. © 2011 IEEE.
date: 2011
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84857088605&doi=10.1109%2fNatPC.2011.6136377&partnerID=40&md5=cadd533c5699cf15001f51361316b3c9
id_number: 10.1109/NatPC.2011.6136377
full_text_status: none
publication: 2011 National Postgraduate Conference - Energy and Sustainability: Exploring the Innovative Minds, NPC 2011
place_of_pub: Perak
refereed: TRUE
isbn: 9781457718847
citation:   Mustafa, M.R.U. and Isa, M.H. and Bhuiyan, R.R.  (2011) Prediction of river suspended sediment load using radial basis function neural network-a case study in Malaysia.  In: UNSPECIFIED.