HUANGHaidong,LIUShitong,WUMeiqiong.Interval Prediction for Water Demand in DMA Based on BiTCN and QRF[J].China Water & Wastewater,2026,42(5):55-62.
Interval Prediction for Water Demand in DMA Based on BiTCN and QRF
China Water & Wastewater[ISSN:1000-4062/CN:12-1073/TU]
volume:
第42卷
Number:
第5期
Page:
55-62
Column:
Date of publication:
2026-03-01
- Keywords:
- interval prediction for water demand; bidirectional time convolutional network; quantile regression forests; Kepler optimization algorithm; district metered area
- Abstract:
- Interval prediction for water demand in district metered area (DMA) is conducive to making appropriate leakage diagnosis for managers of water supply networks. However, ensuring higher coverage probability of the prediction interval while maintaining a lower average width remains a technical challenge in water demand interval prediction. To this end, an interval prediction model combining the bidirectional time convolutional network (BiTCN) with quantile regression forests (QRF) was proposed. First, based on the characteristics of water demand data, a suitable sample set was generated through data reconstruction. Then, the BiTCN model was used to extract deep-level temporal features of the data. Next, the extracted features were input into the QRF model to preliminarily construct the BiTCN-QRF model. Finally, the model was optimized using the Kepler optimization algorithm (KOA) and applied for interval prediction. The effectiveness of the proposed model was validated using data from a DMA located in Du’an County, Guangxi. The results showed that the proposed model could achieve high-quality interval prediction at 95%, 90%, 85%, 80%, and 70% confidence levels. Moreover, its overall performance was better than that of other comparative models. Based on these findings, the proposed model can serve as an effective auxiliary tool for flow-based real-time leakage diagnosis in DMA.
Last Update:
2026-03-01