A Novel Multi-Step Forecasting-Based Approach for Enhanced Burst Detection in Water Distribution Systems

Burst detection in water asset management is a crucial issue in ensuring the efficient and sustainable operation of water distribution systems. For an online burst detection method based on flow time series data, the challenge arises in the variability of anomaly definitions across different datasets, rendering a one-size-fits-all anomaly detection algorithm impossible. Additionally, existing prediction-driven anomaly detection schemes, relying on single-step prediction, face accuracy issues due to susceptibility to input data contamination. In this paper, a novel scheme for burst detection is proposed to address the limitations of existing methods. The approach incorporates a multi-step forecasting model, offering multiple sources for the forecasting, and aggregates the forecasts to establish a common expectation for the data pattern. A metric termed Local Residual Discrepancy (LRD) is proposed to score deviation between predictions and observations. The effectiveness of the proposed method is evaluated through its application to both synthetic and real datasets. Experimental results reveal significant improvements in detection accuracy achieved by the LRD metric, irrespective of the underlying prediction model. This research contributes to the advancement of burst detection methodologies, offering a more robust and versatile approach applicable to varied datasets and prediction models in water distribution systems.