Table 4 Models overall performance
From: Short-term water demand forecasting using hybrid supervised and unsupervised machine learning model
Model | Model identifier | MAPE (%) | NRMSE (%) | ||||||
|---|---|---|---|---|---|---|---|---|---|
1 h | 8 h | 24 h | 7 days | 1 h | 8 h | 24 h | 7 days | ||
SARIMA | S-24 | 15.37 | 17.84 | 17.81 | 21.38 | 18.93 | 18.76 | 19.08 | 24.17 |
S-168 | 14.85 | 15.72 | 17.61 | 18.73 | 17.31 | 17.95 | 19.02 | 19.83 | |
RT | RT | 11.48 | 12.93 | 13.72 | 16.75 | 12.43 | 13.19 | 17.81 | 21.04 |
Hybrid | HYB-N2 | 08.62 | 09.58 | 11.41 | 13.89 | 10.83 | 12.83 | 14.47 | 18.62 |
HYB-N3 | 06.73 | 07.34 | 08.92 | 09.74 | 07.97 | 09.51 | 10.73 | 11.86 | |
HYB-N4 | 04.97 | 06.03 | 06.57 | 06.93 | 05.54 | 06.86 | 08.73 | 09.83 | |
HYB-N5 | 04.84 | 05.82 | 06.12 | 06.80 | 05.18 | 06.24 | 06.78 | 08.62 | |