Forecasting monthly rainfall using autoregressive integrated moving average model (ARIMA): A case study of Fada N'Gourma station in Burkina Faso

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Résumé

Climate related hazards are challenging vulnerable communities and decision makers at any mitigation and adaptationplanning stage. Accurate knowledge on projected climate variables such as rainfall is crucial in setting efficientadaptation strategies. The present study seeks to determine an optimum model to predict rainfall patterns within FadaN’Gourma. To this end, the autoregressive integrated moving average model (ARIMA) were fit to the monthly rainfallrecord for Fada N’Gourma meteorological stations spanning from 1981 to 2021. Then, the Box-Jenkins method has beenapplied under R programming language to identify the appropriate ARIMA (𝑝, 𝑑, 𝑞) ∗ (𝑃, 𝐷, 𝑄) model that fits the rainfallrecords. The stationarity of the dataset has been checked based on Augmented Dicky fuller test. The best model used topredict the next ten-year rainfall was selected based on Akaike information criterion (AIC) and Bayesian informationcriterion (BIC). The efficiency of the model was evaluated by the root mean square errors (RMSE) and the mean squarederror (MSE). The results demonstrate that the ARIMA model (5,0,0)(2,1,0)[12] is an appropriate forecasting tool topredict the monthly rainfall across Fada N’Gourma. Base on this model, rainfall forecast for 10 years was then achieved.The Mann-Kendall trend test for the projected rainfall shows a z = 0.89 and a value of Sen's slope up to 0.88 depictingan increasing trend of the annual rainfall within Fada Gourma by 2030

Mots-clés

Rainfall forecast; ARIMA model; Fada N’Gourma; AIC; BIC