Forecasting of Average Monthly Flow for Two Stations at Khabour River Using Arima And Ann Models

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