Comparative assessment of time series and artificial intelligence models to estimate monthly streamflow: A local and external data analysis approach

Abstract

River flow rates are important for water resources projects. Given this, the current study explored the use of autoregressive (AR) and moving average (MA) techniques as individual time series models and compared them to the same models hybridized with an autoregressive conditional heteroscedasticity (ARCH) model to estimate monthly streamflow. In addition, two artificial intelligence (AI) approaches, namely, multivariate adaptive regression splines (MARS) and gene expression programming (GEP), were explored. The performance of each of these models in estimating monthly streamflow was compared based on local and external data analyses. Using the local data analysis approach, streamflow data at each target station was estimated using observed streamflow at the same station. The external data analysis approach used neighboring station streamflow data to estimate streamflow data for the target station. The Beinerahe Roodbar and Pole Astaneh stations on the Sefidrood River, Iran, as well as the Port Elgin and Walkerton stations on the Saugeen River, Canada, were used as study areas. Upstream and downstream monthly streamflow time series data were used. The performance of all models was compared using three error metrics, including the root mean square error, mean absolute error, and correlation coefficient. The results showed that the hybrid time series models (i.e., AR-ARCH and MA-ARCH) outperformed the conventional AR and MA models. A comparison of all applied models revealed that the hybrid AR-ARCH and MA-ARCH time series models performed better than the AI techniques, when using a local data analysis approach. The external data analysis approach was more accurate for monthly streamflow estimation than the local data analysis approach. To conclude, based on the outcomes of the AI models under the external data analysis approach, nearby data can be used to estimate streamflow of a target station when the target station streamflow data are not available.