气候变化领域集成服务门户(CCPortal): Radial basis function neural networks for reliably forecasting rainfall

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DOI	10.2166/wcc.2012.017
	Radial basis function neural networks for reliably forecasting rainfall
	El-Shafie A.H.; El-Shafie A.; Almukhtar A.; Taha M.R.; El Mazoghi H.G.; Shehata A.
发表日期	2012
ISSN	20402244
起始页码	125
结束页码	138
卷号	3 期号:2
英文摘要	Rainfall forecasting is an interesting task especially in a modern city facing the problem of global warming; in addition rainfall is a necessary input for the analysis and design of hydrologic systems. Most rainfall real-time forecasting models are based on conceptual models simulating the complex hydrological process under climate variability. As there are a lot of variables and parameters with uncertainties and non-linear relationships, the calibration of conceptual or physically based models is often a difficult and time-consuming procedure. Simpler artificial neural network (ANN) forecasts may therefore seem attractive as an alternative model. The present research demonstrates the application of the radial basis function neural network (RBFNN) to rainfall forecasting for Alexandria City, Egypt. A significant feature of the input construction of the RBF network is based on the use of the average 10 year rainfall in each decade to forecast the next year. The results show the capability of the RBF network in forecasting the yearly rainfall and two highest rainfall monsoon months, January and December, compared with other statistical models. Based on these results, the use of the RBF model can be recommended as a viable alternative for forecasting the rainfall based on historical rainfall recorded data. © WSSTP 2012 Journal of Water and Climate Change.
英文关键词	Alexandria - Egypt; Artificial neural network; Radial basis function; Rainfall forecasting
语种	英语
来源期刊	Journal of Water and Climate Change
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/148346
作者单位	Faculty of Engineering, University of Garyounis, Benghazi, Libyan Arab Jamahiriya; Department of Civil Engineering, University kebangsaan Malaysia, Malaysia; KSU Northwest Research-Extension Center, Colby, KS, United States
推荐引用方式 GB/T 7714	El-Shafie A.H.,El-Shafie A.,Almukhtar A.,et al. Radial basis function neural networks for reliably forecasting rainfall[J],2012,3(2).
APA	El-Shafie A.H.,El-Shafie A.,Almukhtar A.,Taha M.R.,El Mazoghi H.G.,&Shehata A..(2012).Radial basis function neural networks for reliably forecasting rainfall.Journal of Water and Climate Change,3(2).
MLA	El-Shafie A.H.,et al."Radial basis function neural networks for reliably forecasting rainfall".Journal of Water and Climate Change 3.2(2012).