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Research Articles

Applying human mobility and water consumption data for short-term water demand forecasting using classical and machine learning models

ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 32-42 | Received 22 May 2019, Accepted 20 Feb 2020, Published online: 10 Mar 2020

Figures & data

Figure 1. Scheme of water demand forecasting process.

Figure 1. Scheme of water demand forecasting process.

Figure 2. Map of DMA sectors. Sectors included in this study are marked with turquoise colour.

Figure 2. Map of DMA sectors. Sectors included in this study are marked with turquoise colour.

Figure 3. Correlation of geolocated data and water usage time-series in DMA 24_Z: (a) series comparison after applying offset and decay parameters. (b) correlation depending on the decay parameter for each DMA. Depicted correlations are calculated for the best offset parameter.

Figure 3. Correlation of geolocated data and water usage time-series in DMA 24_Z: (a) series comparison after applying offset and decay parameters. (b) correlation depending on the decay parameter for each DMA. Depicted correlations are calculated for the best offset parameter.

Table 1. Calculated average MAPE, RMSE and EI for 7-days ahead forecast for all the DMAs with respect to the methods and variants.

Table 2. Calculated average MAPE, RMSE and EI MAPE for 24-hours ahead forecast for all the DMAs with respect to the methods and variants.

Table 3. Averaged share of significant correlation values in the autocorrelation function for 7-days and 24-hours ahead forecasts for all the DMAs with respect to the methods and variants.

Supplemental material

Supplemental Material

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