Parameters Estimate of Autoregressive Moving Average and Autoregressive Integrated Moving Average Models and Compare Their Ability for Inflow Forecasting

By Mohammad Valipour, M. E. Banihabib, S. M. Behbahani. 2012

134 citations recorded by the source corpus.

In this study the ability of Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA) models in forecasting the monthly inflow of Dez dam reservoir located in Teleh Zang station in Dez dam upstream is estimated. ARIMA model has found a widespread application in many practical sciences. In addition, dam reservoir inflow forecasting is done by some methods such as ordinary linear regression, ARMA and artificial neural networks. On the other hand, application of both ARMA and ARIMA models simultaneously in order to compare their ability in autoregressive forecast of monthly inflow of dam reservoir has not been carried out in previous researches. Therefore, this paper attempts to forecast the inflow of Dez dam reservoir by using ARMA and ARIMA models while increasing the number of parameters in order to increase the forecast accuracy to four parameters and comparing them. In ARMA and ARIMA models, the polynomial was derived respectively with four and six parameters to forecast the inflow. By comparing root mean square error of the model, it was determined that ARIMA model can forecast inflow to the Dez reservoir from 12 months ago with lower error than the ARMA model.

Subfields: Electrical and Electronic Engineering, Environmental Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/3ebc37a77619c7ada374fb40b0c1f0d48785e0ba

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In Electrical and Electronic Engineering

In Environmental Engineering

In Management Science and Operations Research

Citations in the corpus

Flood Prediction Using Machine Learning Models: Literature Review (2018, methodology): “The results showed that the hybrid algorithm of WNN performed better with an R 2 equal to 0.9 for daily lead time.”
Forecasting monthly inflow with extreme seasonal variation using the hybrid SARIMA-ANN model (2017, methodology): “A number of researchers have employed such models for making hydrological predictions (Jain et al. 1999; Coulibaly et al. 2000; Xu and Li 2002; Anctil et al. 2004; Kisi 2004; Kisi and Cigizoglu 2005, 2007; Firat and Güngör 2010; Valipour et al. 2012b; Valipour 2016).”
Optimization of neural networks for precipitation analysis in a humid region to detect drought and wet year alarms (2016, methodology): “Banihabib et al. (2012) and Valipour et al. (2012a, 2012b, 2013) forecasted the inflow using ARMA, Auto regressive integrated moving average (ARIMA) and ANN models.”