Print ISSN: 1813-0526

Online ISSN: 2220-1270

Keywords : Time series


Non-Stationarity Identification in Flow Time Series Using Wavelets Transform Technique

Reyan H. Al-Mustafa; Kamel Ali Almohseen

Al-Rafidain Engineering Journal (AREJ), 2021, Volume 26, Issue 2, Pages 267-279
DOI: 10.33899/rengj.2021.130600.1113

 The current study explored the possibility of using  Discrete Wavelets Transform technique (DWT) in diagnosing the  non-stationarity in hydrologic time series, which typically masks the real characteristics of those series. This helps in diagnosing the appropriate model and using it for prediction purposes.
Basically, this manuscript divided into two phases: in the first phase, a defined stochastic linear model parameter,  i.e. (ARMA (1,1)) was developed with known parameters  1 and  of (0.8 and 0.4) respectively. The ACF and PACF analyses before and after intentionally adding some defined deterministic components (such as trend, periodicity, etc.) confirm the capability of (DWT)  in diagnosing those non-stationarity sources. While phase two makes use of (DWT) technique in diagnozing the non-statioarity in an observed flow time series of al-Khabor river, Kurdistan region-Iraq,  where 24 years of flow time series is available. After removing the source of the non-stainarity diagnozed by the proposed method in the data, a stationary model (ARMA (2,1)) has been fitted. The study indicated that the proposed model was distinguished by its capabilities to work in real time, thus, the outcomes of the model is almost following the same pattern of the observed outcomes of the process under study.

Estimation of Reference Evapotranspiration by Predicting Temperature Values Using a Stochastic Model

Dr.Taymoor A. Awchi; Mr.Ihsan F. Hasan

Al-Rafidain Engineering Journal (AREJ), 2013, Volume 21, Issue 3, Pages 82-91
DOI: 10.33899/rengj.2013.75441

Abstract
In this research, the stochastic model (ARIMA) was applied to modeling the monthly temperature values for the area of Mosul, Northern Iraq, by utilizing the time-series data of monthly mean temperature for the period (1995-2010) using the Minitab Software.The performance of the proposed model has been approved through the prediction of monthly temperature values for the years 2009 and 2010 where the correlation coefficient value was (R2=0.99) with the values of the actual data for the same years. Then theproposed model was used topredict the mean monthlytemperature values for the years (2011-2012). Due to its importance in the preservation of water resources and rational use in line with the future state of water in the region, this data was used to estimate the future values of Reference Evapotranspiration (ETo) using different empirical methods basedessentially on temperature. The results ofBlaney-Criddle and Hamon methods showed high correlation with ETovalue calculated by Penman-Monteith model.
Key Words: Reference evapotranspiration, Temperature, ARIMA, Time series.