1 Department of physics and chemistry, Ecole Normale Supérieure of Atakpamé, Togo
2 Laboratory of Applied Hydrology, University of Abomey-Calavi, Benin.
3 Laboratory of Solar Energy, University of Lomé, Togo.
Review Article
World Journal of Advanced Research and Reviews, 2024, 23(02), 754-762
Received on 16 June 2024; revised on 05 August 2024; accepted on 07 August 2024
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.
Streamflow forecasting; Time series models; ARIMA; ARMA.
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Lamboni Batablinlè and Lawin Agnidé Emmanuel. Time series analysis and forecasting of Streamflow at Nangbeto dam in Mono Basin using stochastic approaches. World Journal of Advanced Research and Reviews, 2024, 23(2), 754-762. Article DOI: https://doi.org/10.30574/wjarr.2024.23.2.2375