International Science Index


10008713

Flood Predicting in Karkheh River Basin Using Stochastic ARIMA Model

Abstract:

Floods have huge environmental and economic impact. Therefore, flood prediction is given a lot of attention due to its importance. This study analysed the annual maximum streamflow (discharge) (AMS or AMD) of Karkheh River in Karkheh River Basin for flood predicting using ARIMA model. For this purpose, we use the Box-Jenkins approach, which contains four-stage method model identification, parameter estimation, diagnostic checking and forecasting (predicting). The main tool used in ARIMA modelling was the SAS and SPSS software. Model identification was done by visual inspection on the ACF and PACF. SAS software computed the model parameters using the ML, CLS and ULS methods. The diagnostic checking tests, AIC criterion, RACF graph and RPACF graphs, were used for selected model verification. In this study, the best ARIMA models for Annual Maximum Discharge (AMD) time series was (4,1,1) with their AIC value of 88.87. The RACF and RPACF showed residuals’ independence. To forecast AMD for 10 future years, this model showed the ability of the model to predict floods of the river under study in the Karkheh River Basin. Model accuracy was checked by comparing the predicted and observation series by using coefficient of determination (R2).

References:
[1] Abd Saleh, Z. “Forecasting by Box-Jenkins (ARIMA) Models to Inflow of Haditha Dam,” J. of Babylon Uni., Eng. Sci., 2013, 5(21), 1675-1685.
[2] Box, G.E.P.; Jenkins, G.M. “Time Series Analysis: Forecasting and Control,” Holden Day, San Francisco, California, 1976.
[3] Gargano, R.; Tricarico, C.; Giudice, G. D.; Granata, F. “a stochastic model for daily residential water demand,” Water science and technology: water supply, Available online ws 2016102, 2016.
[4] Hamidi machekposhti K., Sedghi H., Abdolrasoul Telvari A., Babazadeh, H. “Forecasting by Stochastic Models to Inflow of Karkheh Dam at Iran,” Civil Engineering Journal, 2017, Vol. 3, No. 5, 340-350.
[5] Mirzavand, M.; Ghazavi, R. “A stochastic modelling technique for groundwater level forecasting in an arid environment using time series methods,” Water Resources Management, 2015, 29(4), 1315-1328.
[6] Naeem, S. Stochastic Modelling of the Daily ainfall Frequency and Amount. Arabian J. for Science and Eng., 2014, 39(7), 5691–5702.
[7] Nguyen, V. T. V.; Nguyen, T. D.; Cung, A. “A statistical approach to downscaling of sub-daily extreme rainfall processes for climate-related impact studies in urban areas,” Water science and technology: water supply, 2007, 7(2), 183-192.
[8] Nigam, R.; Bux, S.; Nigam, S.; Pardasani, K. R.; Mittal, S. K.; Haque, R. “Time series modeling and forecast of river flow,” Current World Environment, 2009, 4(1), 79-87.
[9] Nigam, R.; Nigam, S.; Mittal, S. K. “The river runoff forecast based on the modeling of time series,” Russian Meteorology and Hydrology, 2014, 39(11), 750-761.
[10] Shakeel, A. M.; Idrees, A. M.; Naeem, H. M.; Sarwar, B. M. “Time Series Modelling of Annual Maximum Flow of River Indus at Sukkur,” Pakistan. Journal of Agricultural Sciences, 1993, 30(1), 36-38.
[11] Srikanthan, R.; McMohan, T. A., Irish, J. L. “Time series analysis of annual flows of Australian streams,” J. Hydrology, 1983.
[12] Stojković, M.; Prohaska, S.; Plavšić, J. “Stochastic structure of annual discharges of large European rivers,” J. Hydrology and Hydromechanics, 2015, 63(1), 63–70.