Prediction of multi-sectoral longitudinal water withdrawals using hierarchical machine learning models

Abstract

Accurate models of water withdrawal are crucial in anticipating the potential water use impacts of drought and climate change. Machine-learning methods are increasingly used in water withdrawal prediction due to their ability to model the complex, nonlinear relationship between water use and potential explanatory factors. However, most machine learning methods do not explicitly address the hierarchical nature of water use data, where multiple observations through time are typically available for multiple facilities, and these facilities can be grouped in a variety of different ways. This work presents a novel approach for prediction of water withdrawals across multiple usage sectors using an ensemble of models fit at different hierarchical levels. A dataset of over 300,000 records of water withdrawal was used to fit models at the facility and sectoral grouping levels, as well as across facility clusters defined by temporal water use characteristics. Using repeated holdout cross validation, it demonstrates that ensemble predictions based on models learned from different data groupings improve withdrawal predictions for 63% of facilities relative to facility-level models. The relative improvement gained by ensemble modeling was greatest for facilities with fewer observations and higher variance, indicating its potential value in predicting withdrawal for facilities with relatively short data records or data quality issues. Inspection of the ensemble weights indicated that cluster level weights were often higher than sector level weights, pointing towards the value of learning from the behavior of facilities with similar water use patterns, even if they are in a different sector.