# Probabilistic water demand forecasting using quantile regression algorithms

arXiv: https://arxiv.org/abs/2104.07985 · HTML: https://mlsanity.com/paper/2104.07985/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 126 references that are in the map.
- First posted on arXiv 16 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [The Evolution of Boosting Algorithms - From Machine Learning to Statistical Modelling](https://mlsanity.com/paper/1403.1452/) (2014)
- [Boosting algorithms in energy research: A systematic review](https://mlsanity.com/paper/2004.07049/) (2020)
- [Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability](https://mlsanity.com/paper/2001.00811/) (2020)
- [Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale](https://mlsanity.com/paper/1909.00247/) (2019)
- [Probabilistic Multi-Step-Ahead Short-Term Water Demand Forecasting with Lasso](https://mlsanity.com/paper/2005.04522/) (2020)

## Abstract (arXiv)

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.