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dc.contributor.authorNadeem Nawazen_US
dc.contributor.authorSobri Harunen_US
dc.contributor.authorRawshan Othmanen_US
dc.contributor.authorArien Heryansyahen_US
dc.date.accessioned2019-05-07T09:57:20Z-
dc.date.available2019-05-07T09:57:20Z-
dc.date.issued2016en_US
dc.identifier.issn0125-2526en_US
dc.identifier.urihttp://it.science.cmu.ac.th/ejournal/dl.php?journal_id=7617en_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/63822-
dc.description.abstractThe gradual transformation of arable lands into urbanized environments in built-up areas is common in fast developing countries like Malaysia. Such changes have a large effect on hydrologic processes in the catchment area, which eventually results in an increase of both the magnitude and frequency of floods in urban areas. Therefore there is a great need of reliable rainfall-runoff models that are able to accurately estimate the discharge for a catchment. So far various physically-based models have been developed to capture the rainfall-runoff process, but the drawback has been the estimation the several numbers of parameters which is quite difficult and time consuming. Recently, artificial intelligence tools are being used because of their capability of modeling complex nonlinear relationships. These tools have been widely used in hydrological time series modeling and prediction. Radial basis function neural network (RBFNN) is a popular artificial intelligence technique that is well used in hydrological modeling. In this study, 30 extreme rainfall-runoff events were extracted from twelve years of hourly rainfall and runoff data. An input selection method based on correlation analysis and mutual information was developed to identify the proper input combinations of rainfall and discharge antecedents. The results obtained by RBFNN model were then compared with a traditionally used statistical model known as auto-regressive moving average with exogenous inputs (ARMAX), as a bench mark. Results showed that RBFNN performance is superior then the traditional statistical model and has good potential to be used as a reliable rainfall-runoff modeling tool.en_US
dc.languageEngen_US
dc.publisherScience Faculty of Chiang Mai Universityen_US
dc.titleApplication of Radial Basis Function Neural Networks for Modeling Rainfall-Runoff Processes: A Case Study of Semenyih River catchment, Malaysiaen_US
dc.typeบทความวารสารen_US
article.title.sourcetitleChiang Mai Journal of Scienceen_US
article.volume43en_US
article.stream.affiliationsDepartment of Hydraulics and Hydrology, Faculty of Civil Engineering, Universiti Teknologi Malaysia,Skudai, 81310 Johor Bahru, Malaysia.en_US
article.stream.affiliationsPetroleum Department, Koya Technical Institute, Erbil Polytechnic University, 44001 Erbil, Kurdistan Regional Government, Iraq.en_US
article.stream.affiliationsFaculty of Water Resources Management, Lasbela University of Agriculture, Water and Marine Sciences, 90150 Uthal, Balochistan, Pakistan.en_US
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