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Artificial neural networks (ANNS) for daily rainfall runoff modelling

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Main Authors: Kuok, King Kuok, Nabil, Bessaih
Other Authors: kkuok100@yahoo.com.sg
Format: Article
Language:English
Published: The Institution of Engineers, Malaysia 2011
Subjects:
Artificial neural networks
Flood forecasting
Rainfall-runoff modeling
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/13737
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spelling my.unimap-137372011-09-13T07:36:43Z Artificial neural networks (ANNS) for daily rainfall runoff modelling Kuok, King Kuok Nabil, Bessaih kkuok100@yahoo.com.sg nabil.bessaieh@mf.gov.dz Artificial neural networks Flood forecasting Rainfall-runoff modeling Link to publisher's homepage at http://www.myiem.org.my/ Rainfall-runoff relationships are among the most complex hydrologic phenomena. Hydrologists have developed conceptual models to simulate runoff but these are composed of a large number of parameters and the interaction is highly complicated. ANN is an information-processing system composed of many nonlinear and densely interconnected neurons. ANN is able to extract the relation between the inputs and outputs of a process without the physics being provided to them. Natural behavior of hydrological processes is appropriate for the application of ANN in hydrology. Nowadays, ANNs are used to build rainfall-runoff models, estimate pier scour. Daily rainfall-runoff model for Sungai Bedup Basin, Sarawak was built using MLP, REC networks. Inputs used are antecedent rainfall, antecedent runoff and rainfall while output was the runoff. ANNs were trained using different training algorithms, learning rates, length of data and number of hidden neurons. All data was collected from DID Sarawak. Results were evaluated using Coefficient of Correlation (R) and Nash-Sutcliffe Coefficient (E2). Results show that ANNs is able to simulate daily runoff with high accuracy (R=0.97). REC performs slightly better than MLP. 2011-09-13T07:36:43Z 2011-09-13T07:36:43Z 2007-09 Article The Journal of the Institution of Engineers, Malaysia, vol. 68(3), 2007, pages 31-42 0126-513X http://myiem.org.my/content/iem_journal_2007-178.aspx http://hdl.handle.net/123456789/13737 en The Institution of Engineers, Malaysia
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Artificial neural networks
Flood forecasting
Rainfall-runoff modeling
spellingShingle Artificial neural networks
Flood forecasting
Rainfall-runoff modeling
Kuok, King Kuok
Nabil, Bessaih
Artificial neural networks (ANNS) for daily rainfall runoff modelling
description Link to publisher's homepage at http://www.myiem.org.my/
author2 kkuok100@yahoo.com.sg
author_facet kkuok100@yahoo.com.sg
Kuok, King Kuok
Nabil, Bessaih
format Article
author Kuok, King Kuok
Nabil, Bessaih
author_sort Kuok, King Kuok
title Artificial neural networks (ANNS) for daily rainfall runoff modelling
title_short Artificial neural networks (ANNS) for daily rainfall runoff modelling
title_full Artificial neural networks (ANNS) for daily rainfall runoff modelling
title_fullStr Artificial neural networks (ANNS) for daily rainfall runoff modelling
title_full_unstemmed Artificial neural networks (ANNS) for daily rainfall runoff modelling
title_sort artificial neural networks (anns) for daily rainfall runoff modelling
publisher The Institution of Engineers, Malaysia
publishDate 2011
url http://dspace.unimap.edu.my/xmlui/handle/123456789/13737
_version_ 1643790953621749760
score 13.222552

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