Burst Detection by Water Demand Nowcasting Based on Exogenous Sensors

Abstract

Bursts of drinking water pipes not only cause loss of drinking water, but also damage below and above ground infrastructure. Short-term water demand forecasting is a valuable tool in burst detection, as deviations between the forecast and actual water demand may indicate a new burst. Many of burst detection methods struggle with false positives due to non-seasonal water consumption as a result of e.g. environmental, economic or demographic exogenous influences, such as weather, holidays, festivities or pandemics. Finding a robust alternative that reduces the false positive rate of burst detection and does not rely on data from exogenous processes is essential. We present such a burst detection method, based on Bayesian ridge regression and Random Sample Consensus. Our exogenous nowcasting method relies on signals of all nearby flow and pressure sensors in the distribution net with the aim to reduce the false positive rate. The method requires neither data of exogenous processes, nor extensive historical data, but only requires one week of historical data per flow/pressure sensor. The exogenous nowcasting method is compared with a common water demand forecasting method for burst detection and shows sufficiently higher Nash-Sutcliffe model efficiencies of 82.7% - 90.6% compared to 57.9% - 77.7%, respectively. These efficiency ranges indicate a more accurate water demand prediction, resulting in more precise burst detection.