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Trinidad has undergone rapid urbanization over the past few decades. Urbanization is accompanied with an increase in the country's demand for water. The forecasting of water demand can give rise to a better understanding of water consumption behaviour across all sectors of economy and therefore aid in effective water demand management. This study compares the application of the seasonal ARIMA, exponential state space (ETS) models, artificial neural network (ANN) models and hybrid combinations of them in developing forecast models for all categories of water consumption for Trinidad. The best forecasting model was selected using the forecasting assessment criterion of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The forecasts were conducted until the end of December 2021. The results of the study show that hybrid model combinations are adequate in forecasting four out of the five categories and the single model, SARIMA, has been found suitable for the domestic category. Forecast plots revealed an increase in water demand until the end of 2021. The study also demonstrates the suitability of hybrid models for forecasting water demand for the island of Trinidad.

Water consumption forecasting models – a case study in Trinidad (Trinidad and Tobago)