A Comparative Study of Time Series and Decision Tree Models for Forecasting Water Levels on the River Benue, Nigeria

The river Benue is vulnerable to flood risks, partly due to the release of water from the Lagdo Dam in Cameroon into Nigeria, as well as high precipitation, resulting in substantial damage and economic losses. Improved flood event prediction is crucial for decision-makers and the population to effectively plan strategies for reducing flood-related losses. This paper presents a comparative study using time series SARIMA and decision tree models applied to monthly water level data for 2011-2016 from Ibi, Makurdi, and Umaisha water stations on the river Benue. Granger causality and correlation tests indicate that water levels at a station closer to the river source are significant in predicting water levels at a station downstream for the decision tree models. Two accuracy metrics, namely mean absolute percentage error (MAPE) and root mean square error (RMSE), were used to assess the models. The prediction results show that the SARIMA (4,0,2)(1,0,1) model is the best choice for forecasting the Ibi station water levels, closely followed by the decision tree. For the Makurdi water station, the decision tree model including the Ibi station water level among the predictors, is best. Finally, for predicting the Umaisha station water level, two decision tree models are best, including the Ibi water level or the Makurdi and Ibi water levels among the predictor variables.