Prediction of Monthly Inflow to Karkhe Reservoir Using ARIMA model

Document Type : Research Article

Author

Faculty of Civil Engineering and Architecture, Shahid Chamran University of Ahvaz, Iran

Abstract

Forecasting future river flow is a critical aspect in efficiently managing water resources, particularly in meeting the diverse downstream requirements of reservoir dams. The significance of predicting inflow to the dam is amplified due to its role in addressing its downstream needs. The present study focuses on predicting the monthly inflow to the Karkheh Reservoir Dam through the utilization of integrated autocorrelated moving average (ARIMA) models, including the seasonal variant (SARIMA). The development of these models involved analyzing 57 years of monthly flow data into the Karkheh dam reservoir. Of this dataset, 47 years were designated for model training, while the remaining 10 years were used for model testing. The determination of optimal ARIMA model parameters involved assessing various combinations of (p, d, q), with selection based on the Akaike information evaluation criterion. Results indicate that the ARIMA model with parameters (8,0,7) yields the lowest Akaike information evaluation criterion. Additionally, recognizing the seasonality in the data, a SARIMA model was constructed and employed for predicting monthly flow into the Karkheh dam reservoir. A comparison of the root mean squared error between the ARIMA and SARIMA methods reveals superior accuracy in predicting monthly flow to the Karkheh dam reservoir with the ARIMA model.

Keywords

Main Subjects

References

[1] Kistenmacher M, Georgakakos AP. Assessment of reservoir system variable forecasts. Water Resources Research. 2015 May;51(5):3437-58.
[2] Ahmad SK, Hossain F. Maximizing energy production from hydropower dams using short-term weather forecasts. Renewable Energy. 2020 Feb 1; 146:1560-77.
[3] Yang S, Yang D, Chen J, Zhao B. Real-time reservoir operation using recurrent neural networks and inflow forecast from a distributed hydrological model. Journal of Hydrology. 2019 Dec 1; 579:124229.
[4] Salas JD. Applied modeling of hydrologic time series. Water Resources Publication; 1980.
[5] Zhao Q, Cai X, Li Y. Determining inflow forecast horizon for reservoir operation. Water Resources Research. 2019 May;55(5):4066-81.
[6] Wang HR, Wang C, Lin X, Kang J. An improved ARIMA model for precipitation simulations. Nonlinear Processes in Geophysics. 2014 Dec 1;21(6):1159-68.
[7] Valipour M. Long‐term runoff study using SARIMA and ARIMA models in the United States. Meteorological Applications. 2015 Jul;22(3):592-8.
[8] Dastorani M, Mirzavand M, Dastorani MT, Sadatinejad SJ. Comparative study among different time series models applied to monthly rainfall forecasting in semi-arid climate condition. Natural Hazards. 2016 Apr; 81:1811-27.
[9] Tadesse KB, Dinka MO. Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa. Journal of water and land development. 2017;35(1):229.
[10] Moeeni H, Bonakdari H, Ebtehaj I. Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach. Journal of Earth System Science. 2017 Mar; 126:1-3.
[11] Elganiny MA, Eldwer AE. Comparison of stochastic models in forecasting monthly streamflow in rivers: A case study of River Nile and its tributaries. Journal of Water Resource and Protection. 2016 Feb 4;8(2):143-53.
[12] Abdellatif ME, Osman YZ, Elkhidir AM. Comparison of artificial neural networks and autoregressive model for inflows forecasting of Roseires Reservoir for better prediction of irrigation water supply in Sudan. International Journal of River Basin Management. 2015 Apr 3;13(2):203-14.
[13] Gupta, A., & Kumar, A. (2022). Two-step daily reservoir inflow prediction using ARIMA-machine learning and ensemble models. Journal of Hydro-environment Research, 45, 39-52.
[14] Valipour M, Banihabib ME, Behbahani SM. Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. Journal of hydrology. 2013 Jan 7; 476:433-41.
[15] Shelke M, Londhe SN, Dixit PR, Kolhe P. Reservoir Inflow Prediction: A Comparison between Semi Distributed Numerical and Artificial Neural Network Modelling. Water Resources Management. 2023 Nov 14:1-7.
[16] Ma Q, Gui X, Xiong B, Li R, Yan L. Applicability Assessment of GPM IMERG Satellite Heavy-Rainfall-Informed Reservoir Short-Term Inflow Forecast and Optimal Operation: A Case Study of Wan’an Reservoir in China. Remote Sensing. 2023 Sep 28;15(19):4741.
[17] Li F, Ma G, Chen S, Huang W. An ensemble modeling approach to forecast daily reservoir inflow using bidirectional long-and short-term memory (Bi-LSTM), variational mode decomposition (VMD), and energy entropy method. Water Resources Management. 2021 Jul; 35:2941-63.
[18] Akaike H. A new look at the statistical model identification. IEEE transactions on automatic control. 1974 Dec;19(6):716-23.
Iranian journal of Ecohydrology
Volume 10, Issue 4
January 2024
Pages 595-606

Files

History

  • Receive Date: 23 May 2023
  • Revise Date: 24 August 2023
  • Accept Date: 24 October 2023
  • First Publish Date: 25 December 2023
  • Publish Date: 15 March 2024

Share

How to cite

Statistics