Time series analysis and forecasting of Streamflow at Nangbeto dam in Mono Basin using stochastic approaches

Accurate prediction of the streamflow has a significantly importance in water resources management. In this study, two time series models, Autoregressive Moving Average model (ARMA) and Autoregressive Integrated Moving Average model (ARIMA) are used for predicting streamflow based on observed monthly streamflow data from 2000 to 2020. The statistics related to first 16 years were used to train the models and last 5 years (2016-2020) were used to forecast. The accuracy of the models was assessed using statistical metrics such as the Nash efficiency (NE), the Root Mean Square Error (RMSE) and mean absolute percentage error (MAPE). The findings show the following values for the performance criteria: The root mean square error was, 52.525 for ARIMA against 59.273 for ARMA. The mean absolute percentage error was 18.245 for ARIMA against 21.642 for ARMA and the Nash efficiency was 0.848 for ARIMA against 0.839 for ARMA. From these results, it is found that ARIMA model performs better than the ARMA models. The results of this research could assist policymakers in managing water resources, agriculture, and mitigating flood risks in the ORB of West Africa.