  Machine and statistical learning algorithms can be reliably automated and
applied at scale. Therefore, they can constitute a considerable asset for
designing practical forecasting systems, such as those related to urban water
demand. Quantile regression algorithms are statistical and machine learning
algorithms that can provide probabilistic forecasts in a straightforward way,
and have not been applied so far for urban water demand forecasting. In this
work, we aim to fill this gap by automating and extensively comparing several
quantile-regression-based practical systems for probabilistic one-day ahead
urban water demand forecasting. For designing the practical systems, we use
five individual algorithms (i.e., the quantile regression, linear boosting,
generalized random forest, gradient boosting machine and quantile regression
neural network algorithms), their mean combiner and their median combiner. The
comparison is conducted by exploiting a large urban water flow dataset, as well
as several types of hydrometeorological time series (which are considered as
exogenous predictor variables in the forecasting setting). The results mostly
favour the practical systems designed using the linear boosting algorithm,
probably due to the presence of trends in the urban water flow time series. The
forecasts of the mean and median combiners are also found to be skilful in
general terms.
