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Minimum input variances for modelling rainfall-runoff using ANN

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Main Authors: Zulkarnain, Hassan, Supiah, Shamsudin, Assoc. Prof., Sobri, Harun, Prof. Madya Dr.
Other Authors: zulkarnain.hassan87@gmail.com
Format: Article
Language:English
Published: Penerbit UTM Press 2015
Subjects:
Artificial neural network
IHACRES
Rainfall-runoff
Runoff
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/40074
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spelling my.unimap-400742015-06-04T02:23:11Z Minimum input variances for modelling rainfall-runoff using ANN Zulkarnain, Hassan Supiah, Shamsudin, Assoc. Prof. Sobri, Harun, Prof. Madya Dr. zulkarnain.hassan87@gmail.com supiah@utm.my sobriharun@utm.my Artificial neural network IHACRES Rainfall-runoff Runoff Link to publisher's homepage at http://www.penerbit.utm.my/ This paper presents the study of possible input variances for modeling the long-term runoff series using artificial neural network (ANN). ANN has the ability to derive the relationship between the inputs and outputs of a PROCESS without the physics being provided to it, and it is believed to be more flexible to be used compared to the conceptual models [1]. Data series from the Kurau River sub-catchment was applied to build the ANN networks and the model was calibrated using the input of rainfall, antecedent rainfall, temperature, antecedent temperature and antecedent runoff. In addition, the results were compared with the conceptual model, named IHACRES. The study reveal that ANN and IHACRES can simulate well for mean runoff but ANN gives a remarkable performance compared to IHACRES, if the model customizes with a good configuration. 2015-06-04T02:23:11Z 2015-06-04T02:23:11Z 2014 Article Jurnal Teknologi (Sciences and Engineering), vol. 69(3), 2014, pages 113-118 0127-9696 (P) 2180-3722 (O) http://www.jurnalteknologi.utm.my/index.php/jurnalteknologi/article/view/3154 http://dspace.unimap.edu.my:80/xmlui/handle/123456789/40074 en Penerbit UTM Press
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 network
IHACRES
Rainfall-runoff
Runoff
spellingShingle Artificial neural network
IHACRES
Rainfall-runoff
Runoff
Zulkarnain, Hassan
Supiah, Shamsudin, Assoc. Prof.
Sobri, Harun, Prof. Madya Dr.
Minimum input variances for modelling rainfall-runoff using ANN
description Link to publisher's homepage at http://www.penerbit.utm.my/
author2 zulkarnain.hassan87@gmail.com
author_facet zulkarnain.hassan87@gmail.com
Zulkarnain, Hassan
Supiah, Shamsudin, Assoc. Prof.
Sobri, Harun, Prof. Madya Dr.
format Article
author Zulkarnain, Hassan
Supiah, Shamsudin, Assoc. Prof.
Sobri, Harun, Prof. Madya Dr.
author_sort Zulkarnain, Hassan
title Minimum input variances for modelling rainfall-runoff using ANN
title_short Minimum input variances for modelling rainfall-runoff using ANN
title_full Minimum input variances for modelling rainfall-runoff using ANN
title_fullStr Minimum input variances for modelling rainfall-runoff using ANN
title_full_unstemmed Minimum input variances for modelling rainfall-runoff using ANN
title_sort minimum input variances for modelling rainfall-runoff using ann
publisher Penerbit UTM Press
publishDate 2015
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/40074
_version_ 1643799260894855168
score 13.214268

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