GENERATING THE RAINFALL TIME SERIES USING ARIMA MODEL IN KURDISTAN REGION, IRAQ
Keywords:
Time series; ARIMA; Rainfall; Forecast; Modelling Rainfall data
Abstract
Generating time series data are an important tool in operations research, as this data is often the basis of model decision-makers. In this study, the annual maximum rainfall (AMR) data from three rainfall stations (i.e., Duhok, Erbil, and Sulaymaniya) located in the Kurdistan Region of Iraq have been used to build auto-regressive integrating moving average (ARIMA) models. For this reason, the rainfall data series from the years 1991 to 2021 was used. The Box-Cox transformation was used to make the rainfall time series stationary and normal. Several statistical tests were used to evaluate how well the successful ARIMA models performed. Results revealed that the most suitable model for the Duhok station was ARMA (0, 3), and for both Erbil and Sulaymaniya stations, it was the ARMA (0, 4) model. The AMR data for the following five years was predicted using these models (2022 to 2026). The study found that in a semi-arid region like the Kurdistan Region of Iraq, the ARIMA models were a useful tool for generating future rainfall.
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References
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W.M. Ridwan, M. Sapitang, A. Aziz, K.F. Kushiar, A.N. Ahmed and A. El-Shafie. Rainfall Forecasting Model using Machine Learning Methods: Case study Terengganu, Malaysia. Ain Shams Engineering Journal, 12 (2), 1651-1663. 2021, DOI: 10.1016/j.asej.2020.09.011.
O. Agha and S. Bagcaci and N. Şarlak. Homogeneity Analysis of Precipitation Series in North Iraq. IOSR Journal of Applied Geology and Geophysics, 5,57-63. DOI:10.9790/0990-0503025763. 2017
O. Al Balasmeh, R. Babbar and T. Karmaker. Trend analysis and ARIMA modeling for forecasting precipitation pattern in Wadi Shueib catchment area in Jordan. Arabian Journal of Geosciences, 12, 27. DOI: https://doi.org/10.1007/s12517-018-4205-z. 2019
T. Alghamdi, K. Elgazzar, M. Bayoumi, T. Sharaf and S.Shah. Forecasting Traffic Congestion Using ARIMA Modeling. 2019 15th International Wireless Communications and Mobile Computing Conference (IWCMC), 1227-1232. DOI: 10.1109/IWCMC.2019.8766698. 2019
D.T. Anh, T.D. Dang, and S. P. Van. Improved Rainfall Prediction using Combined Pre-Processing Methods and Feed-Forward Neural Networks. Multidisciplinary Scientific Journal, 2, 65-83. DOI: https://doi.org/10.3390/j2010006. 2019
H. Akaike. A New Look at the Statistical Model Identification. IEEE Transactions on Automatic Control, 19, 716-723. DOI: 10.1109/TAC.1974.1100705. 1974
M.A. Al-Dabbagh. Modeling the Rainfall-Discharge Relation and Discharge Forecasting for some Rivers in Iraq. Unpublished MSc. Thesis, Mosul University. 2005
T.W. Anderson and D.A. Darling. A Test of Goodness of Fit. Journal of American Statistical Association, 49, 765-769. DOI: https://doi.org/10.2307/2281537. 1954
G.E.P. Box and G.M. Jenkins. Time Series Analysis: Forecasting and Control. Revised Edition, Holden Day, San Francisco. 1976
S. Chattopadhyay and G. Chattopadhyay. Univariate Modelling of Summer-Monsoon Rainfall Time Series: Comparison between ARIMA and ARNN. Comptes Rendus eoscience, 342(2), 100-107. 2010, DOI: 10.1016/j.crte.2009.10.016.
D.A. Dickey and W.A. Fuller. Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association 74 427-431. DOI: https://doi.org/10.2307/2286348. 1979
Geetha and G.M. Nasira. Time-series modelling and forecasting: modelling of rainfall prediction using ARIMA model. HomeInternational Journal of Society Systems Science, 8, 4, 361-372, DOI: https://doi.org/10.1504/IJSSS.2016.081411. 2017
J. Gu, S. Liu, Z. Zhou, S.R. Chalov and Q. Zhuang. A Stacking Ensemble Learning Model for Monthly Rainfall Prediction in the Taihu Basin, China. Water, 14 (492), 2-20. DOI: https://doi.org/10.3390/w14030492. 2022
E. Hajani. Climate Change and its Influence on Design Rainfall At-site in New South Wales State, Australia. Journal of Water and Climate Change 11 (S1), 251-269. 2020
E. Hajani and Z. Klari. Trends Analysis in Rainfall Data Series in Duhok city, Kurdistan Region, Iraq. Modeling Earth Systems and Environment, 1-14. DOI: https://doi.org/10.1007/s40808-022-01354-1. 2022
E. Hajani, K. Shajee, F. Kaled and H. Abdulhaq. Characteristics of Changes in Rainfall Data in the Kurdistan Region Iraq. Arabian Journal of Geosciences, 1-21. DOI: https://doi.org/10.1007/s12517-022-09809-6. 2022
A.B. Koehler and E.S Murphree. A Comparison of the Akaike and Schwarz Criteria for Selecting Model Order. Journal of the Royal Statistical Society. Series C (Applied Statistics), 37, 2, 18795. DOI: https://doi.org/10.2307/2347338. 1988
D. Kwiatkowski, P.C.B. Phillips, P. Schmidt and Y. Shin. Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root. Journal of Econometrics, 54 (1-3), 159-178. DOI: https://doi.org/10.1016/0304-4076(92)90104-Y. 1992
P. Li. Box-Cox Transformations: An Overview. University of Connecticut, Storrs. 2005
G.M. Ljung and G.E. Box. On a Measure of a Lack of Fit in Time Series Models. Biometrika. 65 (2), 297-303. DOI: https://doi.org/10.1093/biomet/65.2.297. 1978
K. Mammas and D.F. Lekkas. Rainfall Generation using Markov Chain Models; Case Study: Central Aegean Sea. Water, 10, 856. DOI: https://doi.org/10.3390/w10070856. 2018
S. Makridakis, S.C. Wheelwright, and R.J. Hyndman. Forecasting: Methods and Applications, 3rd ed., John Wiley, Hoboken, N. J.
Meher J. (2014). Rainfall and Runoff Estimation Using Hydrological Models and ANN Techniques. Doctor Thesis, National Institute of Technology Rourkela-769008, India. 1998
S.S. Shapiro and M.B. Wilk. An Analysis of Variance Test for Normality (complete samples). Biometrika, 52 (3-4), 591-611. DOI: https://doi.org/10.2307/2333709. 1965
R.H. Shumway and D.S. Stoffer. Time Series Analysis and Its Applications with R Examples. Springer Science, Business Media, Berlin. 2005
S. Sukarna, E. Ananda and M. Wahyuni. Rainfall Forecasting Model Using ARIMA and Kalman Filter in Makassar, Indonesia. Journal of Physics Conference Series 2123, 1, 012044. DOI: 10.1088/1742-6596/2123/1/012044. 2021
M. Peña, A. Va´zquez-Patiño, D. Zhiña, M. Montenegro and A. Alex. Improved Rainfall Prediction through Nonlinear Autoregressive Network with Exogenous Variables: A Case Study in Andes High Mountain Region. Hindawi Advances in Meteorology, 1828319, 17. DOI: https://doi.org/10.1155/2020/1828319. 2020
P.C.B. Phillips and P. Perron. Testing for a Unit Root in Time Series Regression. Biometrika, 75 (2), 335-346. DOI: https://doi.org/10.2307/2336182. 1988
W.M. Ridwan, M. Sapitang, A. Aziz, K.F. Kushiar, A.N. Ahmed and A. El-Shafie. Rainfall Forecasting Model using Machine Learning Methods: Case study Terengganu, Malaysia. Ain Shams Engineering Journal, 12 (2), 1651-1663. 2021, DOI: 10.1016/j.asej.2020.09.011.
Published
2023-12-24
How to Cite
SARMA , G., & HAJANI , E. (2023). GENERATING THE RAINFALL TIME SERIES USING ARIMA MODEL IN KURDISTAN REGION, IRAQ. Journal of Duhok University, 26(2), 784-794. https://doi.org/10.26682/csjuod.2023.26.2.69
Section
Pure and Engineering Sciences
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