Probabilistic water demand forecasting using quantile regression algorithms

TL;DR

This paper introduces and compares several quantile regression algorithms for probabilistic urban water demand forecasting, demonstrating their effectiveness and identifying linear boosting as particularly suitable for this task.

Contribution

It fills the gap by applying and extensively comparing multiple quantile regression methods for one-day ahead urban water demand forecasting.

Findings

Linear boosting performs best among tested algorithms.

Mean and median combiners provide skillful probabilistic forecasts.

Quantile regression methods are effective for urban water demand prediction.

Abstract

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.