Print Email Facebook Twitter Water Demand Forecasting Accuracy and Influencing Factors at Different Spatial Scales Using a Gradient Boosting Machine Title Water Demand Forecasting Accuracy and Influencing Factors at Different Spatial Scales Using a Gradient Boosting Machine Author Xenochristou, Maria (University of Exeter) Hutton, C. (Wessex Water) Hofman, J. (University of Bath) Kapelan, Z. (TU Delft Sanitary Engineering; University of Exeter) Date 2020 Abstract Understanding, comparing, and accurately predicting water demand at different spatial scales is an important goal that will allow effective targeting of the appropriate operational and conservation efforts under an uncertain future. This study uses data relating to water consumption available at the household level, as well as postcode locations, household characteristics, and weather data in order to identify the relationships between spatial scale, influencing factors, and forecasting accuracy. For this purpose, a Gradient Boosting Machine (GBM) is used to predict water demand 1–7 days into the future. Results show an exponential decay in prediction accuracy from a Mean Absolute Percentage Error (MAPE) of 3.2% to 17%, for a reduction in group size from 600 to 5 households. Adding explanatory variables to the forecasting model reduces the MAPE up to 20% for the peak days and smaller household groups (20–56 households), whereas for larger aggregations of properties (100–804 households), the range of improvement is much smaller (up to 1.2%). Results also show that certain types of input variables (past consumption and household characteristics) become more important for smaller aggregations of properties, whereas others (weather data) become less important. Subject Gradient Boosting Machinessmart demand dataspatial scaleswater demand forecastingweather influence To reference this document use: http://resolver.tudelft.nl/uuid:4b04982f-0827-43e1-9c82-4aa4c1e88017 DOI https://doi.org/10.1029/2019WR026304 Embargo date 2020-12-29 ISSN 0043-1397 Source Water Resources Research, 56 (8), 1-15 Part of collection Institutional Repository Document type journal article Rights © 2020 Maria Xenochristou, C. Hutton, J. Hofman, Z. Kapelan Files PDF 2019WR026304.pdf 2 MB Close viewer /islandora/object/uuid:4b04982f-0827-43e1-9c82-4aa4c1e88017/datastream/OBJ/view