1. bookVolume 10 (2014): Issue 4 (December 2014)
Journal Details
License
Format
Journal
eISSN
2784-1391
First Published
12 Apr 2013
Publication timeframe
4 times per year
Languages
English
Open Access

R Language: Statistical Computing and Graphics for Modeling Hydrologic Time Series

Published Online: 03 Mar 2015
Volume & Issue: Volume 10 (2014) - Issue 4 (December 2014)
Page range: 9 - 18
Journal Details
License
Format
Journal
eISSN
2784-1391
First Published
12 Apr 2013
Publication timeframe
4 times per year
Languages
English
Abstract

The analysis and management of Hydrology time series is used for the development of models that allow predictions on future evolutions. After identifying the trends and the seasonal components, a residual analysis can be done to correlate them and make a prediction based on a statistical model. Programming language R contains multiple packages for time series analysis: ‘hydroTSM’ package is adapted to the time series used in Hydrology, package ‘TSA’ is used for general interpolation and statistical analysis, while the ‘forecast’ package includes exponential smoothing, all having outstanding capabilities in the graphical representation of time series. The purpose of this paper is to present some applications in which we use time series of precipitation and temperature from Fagaras in the time period 1966-1982. The data was analyzed and modeled by using the R language.

Keywords

[1] Sayemuzzaman, M., Jha, MK. (2014). Seasonal and annual precipitation time series trend analysis in North Carolina, Atmospheric Research, Volume 137, pp 183-194. http://dx.doi.org/10.1016/j.atmosres.2013.10.01210.1016/j.atmosres.2013.10.012Search in Google Scholar

[2] IPCC Fifth Assessment Report: Climate Change. (2013) , http://www.ipcc.ch/report/ar5/Search in Google Scholar

[3] Climate change Romania, http://www.climateadaptation.eu/romania/climate-change/Search in Google Scholar

[4] Raport de mediu-Plan Urbanistic General Municipiul Fagaras, http://www.primaria-fagaras.ro/urbanism/PUG-2013/raport%20mediu%20revizuit%20mai%202013.pdfSearch in Google Scholar

[5] The Comprehensive R Archive Network. http://cran.r-project.org/Search in Google Scholar

[6] Cryer, J. D., Chan, K-S. (2008).Time Series Analysis with Applications in R, Springer10.1007/978-0-387-75959-3Search in Google Scholar

[7] Brockwell, P. J., Davis R. A. (2002). Introduction to Time Series and Forecasting. Springer-Verlag New York, Inc10.1007/b97391Search in Google Scholar

[8] Ljung, G. M.; Box, G. E. P. (1978). On a Measure of a Lack of Fit in Time Series Models. Biometrika 65 (2), pp 297-303.10.1093/biomet/65.2.297Search in Google Scholar

[9] Barbulescu A., Deguenon, J. (2011). Mathematical models for extreme monthly precipitation, Ovidius University Annals, Series: Civil Engineering, issue 13, pp. 93 - 104, http://revista-constructii.univovidius.ro/doc/anale/2011.pdfSearch in Google Scholar

[10] Cowpertwait, P. S.P. (2006). Introductory Time Series with R, Springer Science+Business mediaSearch in Google Scholar

[11]Coghlan, A. (2014). Using R for Time Series Analysis, https://media.readthedocs.org/pdf/a-little-book-of-r-fortime-series/latest/a-little-book-of-r-for-time-series.pdfSearch in Google Scholar

[12] Miroiu, M., Petrehus, V., Zbaganu G. (2008-2011): Initiere in R pentru persoane cu pregatire matematica, POSDRU/56/1.2/S/32768Search in Google Scholar

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