Comparative study among different time series models applied to monthly rainfall forecasting in semi-arid climate condition

Mostafa Dastorani (), Mohammad Mirzavand (), Mohammad Taghi Dastorani () and Seyyed Javad Sadatinejad ()
Additional contact information
Mostafa Dastorani: University of Kashan
Mohammad Mirzavand: University of Kashan
Mohammad Taghi Dastorani: Ferdowsi University of Mashhad (FUM)
Seyyed Javad Sadatinejad: University of Tehran

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2016, vol. 81, issue 3, No 20, 1827 pages

Abstract: Abstract The aim of this study is to investigate the ability of different time series models in forecasting monthly rainfall. In order to do this, monthly rainfall data were collected from 9 rainfall stations in North Khorasan province (North east of Iran) from 1989 to 2012. R software was used to predict the highest rainfall in these 9 rain gage stations for the time period 2002–2012 using monthly highest rainfall data of 1989–2002. In this study, AR, MA, ARMA, ARIMA, and SARIMA with 11 different structures based on trial and error were examined. Because the trend, seasonal and jump components are deterministic components, it is not necessary to model these components, but modeling of random component is very important for rainfall forecasting. So, the main data series was decomposed (for AR, MA and ARMA models) and the random part has been modeled. After that, the random component was collected with the seasonal and trend component and the amount of rainfall was simulated. But for ARIMA and SARIMA, models fitted on original series. The result showed that in 33 % of data MA(2), in 22 % of data AR(1) and ARMA(2, 1) and in 11.11 % of data MA(1) and ARIMA(1, 1, 2) had the best performance in monthly rainfall forecasting. On the other hand, best time series model by change of data could vary. So, it is important to assess all the time series models for any area and any hydrological parameter.

Keywords: Rainfall forecasting; Time series; AR; MA; ARMA; ARIMA (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://link.springer.com/10.1007/s11069-016-2163-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:81:y:2016:i:3:d:10.1007_s11069-016-2163-x

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-016-2163-x

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().