Monthly Inflow Forecasting of Three Multi-Purpose Reservoirs

Nastasia F. Margini, Nadjadji Anwar, Wasis Wardoyo, D. D. Prastyo, Zulkifli Yusop


The need for inflow discharge forecasts is the first step in the process of integrating water management. To overcome this problem, a discharge forecasting analysis system is needed. This paper adopts a seasonal autoregressive integrated moving average forecasting analysis model, SARIMA. This method was chosen and then applied to the inflow discharge data of the Wonorejo Reservoir to obtain the best model. Determination of the best model through forecasting performance measures using the minimum Mean Square Error (MSE). The best model has an MSE of 11.79 on discharge data for 18 years from 2003 to 2020. The best forecast model is then evaluated on the Bendo Reservoir and Sampean Reservoir. The difference between this paper and others is that one model is used for three different multi-purpose reservoirs and obtains feasible results for each reservoir. Therefore, the authors conclude that the forecasting results of the SARIMA (1,0,0)(0,1,1)12 model can be applied to Wonorejo Reservoir, Bendo Reservoir, and Sampean Reservoir in East Java Province, Indonesia. The best model from the analysis process is that in the Wonorejo Reservoir, the inflow prediction is satisfactory for the next five years, the Sampean Reservoir for the next four years, and the Bendo Reservoir is the best forecast for the next three years. The results of this forecasting model can be used to analyze the optimization of multi-purpose reservoir management and reduce the risk of reservoir water shortages. Further research can be carried out to achieve extreme values in inflow discharge forecasting.


Discharge forecasting; multi-purpose reservoir; SARIMA; inflow reservoir forecasting.

Full Text:



J. Zhang et al., “Daily runoff forecasting by deep recursive neural network,†J. Hydrol., vol. 596, no. February, p. 126067, 2021, doi: 10.1016/j.jhydrol.2021.126067.

M. F. Allawi, O. Jaafar, F. Mohamad Hamzah, and A. El-Shafie, “Novel reservoir system simulation procedure for gap minimization between water supply and demand,†J. Clean. Prod., vol. 206, pp. 928–943, 2019, doi: 10.1016/j.jclepro.2018.09.237.

G. Yang, S. Guo, P. Liu, and P. Block, “Integration and Evaluation of Forecast-Informed Multiobjective Reservoir Operations,†J. Water Resour. Plan. Manag., vol. 146, no. 6, pp. 1–12, 2020, doi: 10.1061/(ASCE)WR.1943-5452.0001229.

N. F. Margini, W. Wardoyo, and N. Anwar, “Variability of Discharge Inflow in Wonorejo Reservoir, Indonesia,†IOP Conf. Ser. Earth Environ. Sci., vol. 999, no. 1, p. 012014, 2022, doi: 10.1088/1755-1315/999/1/012014.

J. M. Wolfand et al., “Balancing water reuse and ecological support goals in an effluent dominated river,†J. Hydrol. X, vol. 15, p. 100124, 2022, doi: 10.1016/j.hydroa.2022.100124.

A. Arlimasita and U. Lasminto, “Sensitivity analysis of rainfall-runoff model in malino sub-watershed,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 10, no. 4, pp. 1578–1583, 2020, doi: 10.18517/ijaseit.10.4.12803.

K. Wang, H. Shi, J. Chen, and T. Li, “An improved operation-based reservoir scheme integrated with Variable Infiltration Capacity model for multiyear and multipurpose reservoirs,†J. Hydrol., vol. 571, no. April, pp. 365–375, 2019, doi: 10.1016/j.jhydrol.2019.02.006.

Y. Bai, S. P. Langarudi, and A. G. Fernald, “System dynamics modeling for evaluating regional hydrologic and economic effects of irrigation efficiency policy,†Hydrology, vol. 8, no. 2, 2021, doi: 10.3390/hydrology8020061.

N. Zougagh, A. Charkaoui, and A. Echchatbi, “Prediction models of demand in supply chain,†Procedia Comput. Sci., vol. 177, pp. 462–467, 2020, doi: 10.1016/j.procs.2020.10.063.

N. A. H. M. Thamer and N. S. I. Alsharabi, “Predicting Time Series of Temperature in Nineveh Using The Conversion Function Models,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 11, no. 2, pp. 572–580, 2021, doi: 10.18517/ijaseit.11.2.14085.

K. Christian, A. F. V. Roy, D. Yudianto, and D. Zhang, “Application of optimized Support Vector Machine in monthly streamflow forecasting: using Autocorrelation Function for input variables estimation,†Sustain. Water Resour. Manag., vol. 7, no. 3, pp. 1–14, 2021, doi: 10.1007/s40899-021-00506-y.

A. Danandeh Mehr, S. Ghadimi, H. Marttila, and A. Torabi Haghighi, “A new evolutionary time series model for streamflow forecasting in boreal lake-river systems,†Theor. Appl. Climatol., vol. 148, no. 1–2, pp. 255–268, 2022, doi: 10.1007/s00704-022-03939-3.

S. Vavoulogiannis, T. Iliopoulou, P. Dimitriadis, and D. Koutsoyiannis, “Multiscale temporal irreversibility of streamflow and its stochastic modelling,†Hydrology, vol. 8, no. 2, 2021, doi: 10.3390/hydrology8020063.

Y. Aufar, I. S. Sitanggang, and Annisa, “Parameter Optimization of Rainfall-runoff Model GR4J using Particle Swarm Optimization on Planting Calendar,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 10, no. 6, pp. 2575–2581, 2020, doi: 10.18517/ijaseit.10.6.9110.

G. Tegegne and Y. O. Kim, “Representing inflow uncertainty for the development of monthly reservoir operations using genetic algorithms,†J. Hydrol., vol. 586, no. November 2019, p. 124876, 2020, doi: 10.1016/j.jhydrol.2020.124876.

A. K. Misra, G. Agrawal, and K. Lata, “Modeling the influence of human population and human population augmented pollution on rainfall,†Discret. Contin. Dyn. Syst. - B, vol. 0, no. 0, p. 0, 2021, doi: 10.3934/dcdsb.2021169.

T. Berhane, N. Shibabaw, G. Awgichew, and T. Kebede, “Option pricing of weather derivatives based on a stochastic daily rainfall model with Analogue Year component,†Heliyon, vol. 6, no. 1, 2020, doi: 10.1016/j.heliyon.2020.e03212.

M. Pamirbek K, C. X, S. Aher, A. Salamat, P. Deshmukh, and C. Temirbek, “Analysis of Discharge Variability in the Naryn River Basin, Kyrgyzstan,†Hydrospatial Anal., vol. 3, no. 2, pp. 90–106, 2020, doi: 10.21523/gcj3.19030204.

K. R. Basin, “Trend Analysis of Hydroclimatic Variables in the,†2019.

“Time Series Analysis Forecasting and Control . by George E . P . Box ; Gwilym M . Jenkins Review by : I . M . Chakravarti Published by : American Statistical Association Stable URL : . The Methods and Materials of Demog,†vol. 68, no. 342, pp. 493–494, 2014.

M. Mirzavand and R. Ghazavi, “A Stochastic Modelling Technique for Groundwater Level Forecasting in an Arid Environment Using Time Series Methods,†Water Resour. Manag., vol. 29, no. 4, pp. 1315–1328, 2015, doi: 10.1007/s11269-014-0875-9.

L. Martínez-Acosta, J. P. Medrano-Barboza, Ã. López-Ramos, J. F. R. López, and Ã. A. López-Lambraño, “SARIMA approach to generating synthetic monthly rainfall in the Sinú river watershed in Colombia,†Atmosphere (Basel)., vol. 11, no. 6, pp. 1–16, 2020, doi: 10.3390/atmos11060602.

M. DeluarJahan Moloy, M. Chowdhury, M. Binyamin, and S. Kumar Mondal, “Using SARIMA Approach to Modeling and Forecasting Monthly Rainfall in Bangladesh,†Ijsar, vol. 5, no. 5, pp. 6–14, 2018.

K. B. Tadesse and M. O. Dinka, “Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa,†J. Water L. Dev., vol. 35, no. 1, pp. 229–236, 2017, doi: 10.1515/jwld-2017-0088.

W. Sri Rahayu, P. Tri Juwono, and W. Soetopo, “Discharge prediction of Amprong river using the ARIMA (autoregressive integrated moving average) model,†IOP Conf. Ser. Earth Environ. Sci., vol. 437, no. 1, 2020, doi: 10.1088/1755-1315/437/1/012032.

M. Dastorani, M. Mirzavand, M. T. Dastorani, and S. J. Sadatinejad, “Comparative study among different time series models applied to monthly rainfall forecasting in semi-arid climate condition,†Nat. Hazards, vol. 81, no. 3, pp. 1811–1827, 2016, doi: 10.1007/s11069-016-2163-x.

R. M. Adnan, X. Yuan, O. Kisi, and Y. Yuan, “Streamflow forecasting of Astore River with Seasonal Autoregressive Integrated Moving Average model,†Eur. Sci. Journal, ESJ, vol. 13, no. 12, p. 145, 2017, doi: 10.19044/esj.2017.v13n12p145.

J. D. Restrepo, R. Escobar, and M. Tosic, “Fluvial fluxes from the Magdalena River into Cartagena Bay, Caribbean Colombia: Trends, future scenarios, and connections with upstream human impacts,†Geomorphology, vol. 302, pp. 92–105, 2018, doi: 10.1016/j.geomorph.2016.11.007.

C. C. Nwokike, B. C. Offorha, M. Obubu, C. B. Ugoala, and H. I. Ukomah, “Comparing SANN and SARIMA for forecasting frequency of monthly rainfall in Umuahia,†Sci. African, vol. 10, p. e00621, 2020, doi: 10.1016/j.sciaf.2020.e00621.

A. S. Azad et al., “Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study,†Sustain., vol. 14, no. 3, 2022, doi: 10.3390/su14031843.

A. Masduqi, A. R. Nugroho, and S. A. Wilujeng, “Solution to water scarcity in the eastern indonesia: A case study of the lembata regency,†Int. J. GEOMATE, vol. 19, no. 71, pp. 69–76, 2020, doi: 10.21660/2020.71.30531.

N. F. Margini, N. Anwar, and W. Wardoyo, “The analysis of water balanced in Bendo Reservoir using Dynamic System,†IOP Conf. Ser. Mater. Sci. Eng., vol. 930, no. 1, 2020, doi: 10.1088/1757-899X/930/1/012081.

G. E. P. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time series analysis: forecasting and control. John Wiley & Sons, 2015.

M. Pini, A. Scalvini, M. U. Liaqat, R. Ranzi, I. Serina, and T. Mehmood, “Evaluation of machine learning techniques for inflow prediction in Lake Como, Italy,†Procedia Comput. Sci., vol. 176, pp. 918–927, 2020, doi: 10.1016/j.procs.2020.09.087.

M. A. Shehzad, A. Bashir, M. Noor Ul Amin, S. K. Khosa, M. Aslam, and Z. Ahmad, “Reservoir Inflow Prediction by Employing Response Surface-Based Models Conjunction with Wavelet and Bootstrap Techniques,†Math. Probl. Eng., vol. 2021, 2021, doi: 10.1155/2021/4086918.



  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development