Time Series Analysis of Nyala Rainfall Using ARIMA Method

Mohamed , Tariq Mahgoub; ibrahim , abbas abdalla

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dc.contributor.author	Mohamed , Tariq Mahgoub
dc.contributor.author	ibrahim , abbas abdalla
dc.date.accessioned	2017-04-25T07:50:30Z
dc.date.available	2017-04-25T07:50:30Z
dc.date.issued	2016
dc.identifier.citation	Mohamed , Tariq Mahgoub . Time Series Analysis of Nyala Rainfall Using ARIMA Method \ Tariq Mahgoub Mohamed , abbas abdalla ibrahim .- Journal of Engineering and Computer Sciences (ECS) .- vol 17 , no1.- 2016.- article	en_US
dc.identifier.issn	ISSN 1605-427X
dc.identifier.uri	http://repository.sustech.edu/handle/123456789/16642
dc.description	article	en_US
dc.description.abstract	This paper presents linear stochastic models known as multiplicative seasonal autoregressive integrated moving average model (SARIMA).The model is used to simulate monthly rainfall in Nyala station, Sudan. For the analysis, monthly rainfall data for the years 1971–2010 were used. The seasonality observed in Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) plots of monthly rainfall data was removed using first order seasonal differencing prior to the development of the SARIMA model. Interestingly, the SARIMA (0,0,0)x(0,1,1)12 model developed was found to be most suitable for simulating monthly rainfall over Nyala station. This model is considered appropriate to forecast the monthly rainfall to assist decision makers to establish priorities for water demand, storage, distribution and disaster management.	en_US
dc.description.sponsorship	Sudan University of Science and Technology	en_US
dc.language.iso	en_US	en_US
dc.publisher	Sudan University of Science and Technology	en_US
dc.subject	Sudan, Nyala station, rainfall, Sarima model, ACF, PACF	en_US
dc.title	Time Series Analysis of Nyala Rainfall Using ARIMA Method	en_US
dc.type	Article	en_US