FORECASTING OF AVERAGE MONTHLY FLOW FOR TWO STATIONS AT KHABOUR RIVER USING ARIMA AND ANN MODELS

AVA S.  SAADI; KHALID M.   KHIDIR; SHAKER A.  JALIL

doi:10.26682/sjuod.2023.26.1.30

AVA S. SAADI Dept. of Water Resources Engineering, College of Engineering, University of Duhok, Kurdistan Region-Iraq
KHALID M. KHIDIR Dept. of Water Resources Engineering, College of Engineering, University of Duhok, Kurdistan Region-Iraq
SHAKER A. JALIL Dept. of Water Resources Engineering, College of Engineering, University of Duhok, Kurdistan Region-Iraq

DOI: https://doi.org/10.26682/sjuod.2023.26.1.30

Keywords: Time Series, Stream Flow, Forecasting Models, ARIMA, and ANN

Abstract

In this study, autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models were applied to predict the average monthly flow time series of two stations, named Begova and Chemcermo, for the Khabour River. The analysis of the time series was performed using several criteria and tests. The autocorrelation function (ACF) and partial autocorrelation function (PACF) were applied to check the accuracy of the ARIMA model. The Akaike (AIC) and Bayesian (BIC) equations were performed to determine the optimum model for prediction, which depended on the lowest AIC and BIC values. Applied test results show that the models of order ARIMA (0,0,0)(3,0,0)₁₂ and ARIMA((0,0,5)(5,1,4)₁₂ have higher acceptance compared to the other models for predicting the average monthly flow for Begova and Chemecermo stations, respectively. An ANN model of type multilayer perceptron method (ANN-MLP) was used for predicting the average monthly flow of Begova and Chemecermo stations, where the best models found are MLP (5,3,1) and MLP (9,7,1), respectively. Different statistical tests were applied and showed that the efficiency of the ANN model was better than the ARIMA model, with deterministic coefficients of 0.914 and 0.876 compared to 0.854, and 0.852 for the ARIMA for the monthly time series of Begova and Chemecermo stations, respectively

Downloads

Download data is not yet available.

References

ADNAN, R. M., YUAN, X., KISI, O. & CURTEF, V. 2017. Application of time series models for streamflow forecasting. Civil and Environmental Research, 9, 56-63.
AKAIKE, H. 1974. A new look at the statistical model identification. IEEE transactions on automatic control, 19, 716-723.
AL-JUBOORI, A. M. & GUVEN, A. 2016. A stepwise model to predict monthly streamflow. Journal of Hydrology, 543, 283-292.
AL-SAATI, N. H., OMRAN, I. I., SALMAN, A. A., AL-SAATI, Z. & HASHIM, K. S. 2021. Statistical modeling of monthly streamflow using time series and artificial neural network models: Hindiya Barrage as a case study. Water Practice and Technology, 16, 681-691.
ALADE, O. M., SOWUNMI, O. Y., MISRA, S., MASKELIŪNAS, R. & DAMAŠEVIČIUS, R. A neural network based expert system for the diagnosis of diabetes mellitus. International Conference on Information Technology Science, 2017. Springer, 14-22.
ALQURAISH, M. M. & KHADR, M. 2021. Remote-Sensing-Based Streamflow Forecasting Using Artificial Neural Network and Support Vector Machine Models. Remote Sensing, 13, 4147.
ARISOY, E., SAINATH, T. N., KINGSBURY, B. & RAMABHADRAN, B. Deep neural network language models. Proceedings of the NAACL-HLT 2012 Workshop: Will We Ever Really Replace the N-gram Model? On the Future of Language Modeling for HLT, 2012. 20-28.
BOX, G. E. & COX, D. R. 1964. An analysis of transformations. Journal of the Royal Statistical Society: Series B (Methodological), 26, 211-243.
CHOW, G. C. 1984. Random and changing coefficient models. Handbook of econometrics, 2, 1213-1245.
ESSAM, Y., HUANG, Y. F., NG, J. L., BIRIMA, A. H., AHMED, A. N. & EL-SHAFIE, A. 2022. Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms. Scientific Reports, 12, 3883.
FASHAE, O. A., OLUSOLA, A. O., NDUBUISI, I. & UDOMBOSO, C. G. 2019. Comparing ANN and ARIMA model in predicting the discharge of River Opeki from 2010 to 2020. River research and applications, 35, 169-177.
FRAUSTO-SOLIS, J., PITA, E. & LAGUNAS, J. Short-term streamflow forecasting: ARIMA vs neural networks. American Conference on Applied Mathematics (MATH'08), Harvard, Massachusetts, USA, 2008. 402-407.
GRIMALDI, S. 2004. Linear parametric models applied to daily hydrological series. Journal of Hydrologic Engineering, 9, 383-391.
HAITOVSKY, Y. 1968. Missing data in regression analysis. Journal of the Royal Statistical Society: Series B (Methodological), 30, 67-82.
HUMPHREY, G. B., GIBBS, M. S., DANDY, G. C. & MAIER, H. R. 2016. A hybrid approach to monthly streamflow forecasting: integrating hydrological model outputs into a Bayesian artificial neural network. Journal of Hydrology, 540, 623-640.
HUSSAIN, M. & MAHMUD, I. 2019. pyMannKendall: a python package for non parametric Mann Kendall family of trend tests. Journal of Open Source Software, 4, 1556.
KASSEM, A. A., RAHEEM, A. M. & KHIDIR, K. M. 2020. Daily streamflow prediction for khazir river basin using ARIMA and ANN models. Zanco Journal of Pure and Applied Sciences, 32, 30-39.
KHADIR, K. M., RAHEEM, A. M. & IBRAHIM, S. A. 2018. Stochastic Models for the Maximum and Minimum Daily Flows of Tigris and Khabur Rivers. ZANCO Journal of Pure and Applied Sciences, 30, 50-61.
KHIDIR, K. M. 1980. Water Resources Evaluation For The Tigris River Basin North of Mosul.
KURUNÇ, A., YÜREKLI, K. & CEVIK, O. 2005. Performance of two stochastic approaches for forecasting water quality and streamflow data from Yeşilιrmak River, Turkey. Environmental Modelling & Software, 20, 1195-1200.
LOH, P.-L. & WAINWRIGHT, M. J. 2011. High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity. Advances in neural information processing systems, 24.
MIRZAVAND, M. & GHAZAVI, R. 2015. A stochastic modelling technique for groundwater level forecasting in an arid environment using time series methods. Water resources management, 29, 1315-1328.
MODARES, R. & ESLAMIAN, S. 2006. STREAMFLOW TIME SERIES MODELING OF ZAYANDEHRUD RIVER; RESEARCH NOTE.
NACAR, S., HıNıS, M. A. & KANKAL, M. 2018. Forecasting daily streamflow discharges using various neural network models and training algorithms. KSCE Journal of Civil Engineering, 22, 3676-3685.
PALIT, A. K. & POPOVIC, D. 2006. Computational intelligence in time series forecasting: theory and engineering applications, Springer Science & Business Media.
PRIESTLEY, M. & RAO, T. S. 1969. A test for non‐stationarity of time‐series. Journal of the Royal Statistical Society: Series B (Methodological), 31, 140-149.
R MUHAMAD, J. & N HASSAN, J. 2005. Khabur River flow modeling using artificial neural networks. Al-Rafidain Engineering Journal (AREJ), 13, 33-42.
SALEH, D. K. 2010. Stream gage descriptions and streamflow statistics for sites in the Tigris River and Euphrates River basins, Iraq, US Department of the Interior, US Geological Survey Reston, VA, USA.
SCHWARZ, G. 1978. Estimating the dimension of a model. The annals of statistics, 461-464.
SEO, M., KEMBHAVI, A., FARHADI, A. & HAJISHIRZI, H. 2016. Bidirectional attention flow for machine comprehension. arXiv preprint arXiv:1611.01603.
TONG, L. I. & LIANG, Y. H. 2005. Forecasting field failure data for repairable systems using neural networks and SARIMA model. International Journal of Quality & Reliability Management.
WANG, W., VAN GELDER, P. H. & VRIJLING, J. 2008. Measuring predictability of daily streamflow processes based on univariate time series model.
ZACHARIS, N. Z. 2016. Predicting student academic performance in blended learning using artificial neural networks. International Journal of Artificial Intelligence and Applications, 7, 17-29.