Burst Detection by Water Demand Nowcasting Based on Exogenous Sensors

Caspar V C Geelen,Doekle R Yntema,Karel J Keesman,Jaap Molenaar

doi:10.1007/s11269-021-02768-9

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.

Highlights

Water distribution networks form an extensive and complex underground infrastructure, coping with a water demand that changes over time and per location
autoregressive moving average (ARMA) models are suitable for short-term forecasts of water demand, as these models are strong in capturing the specific periodic patterns of water consumption
Ordinary ARMA models do not take into account these exogenous processes, resulting in an increased false positive rate of burst detection (Billings and Jones 2008)

Summary

Introduction

Water distribution networks form an extensive and complex underground infrastructure, coping with a water demand that changes over time and per location. Due to this complexity, optimal management and operation of the distribution network is a challenging task. Significant deviations between the forecasted and the current water consumption indicate a burst, if a suitable and accurate forecasting method is used (Hutton and Kapelan 2015a). The most frequently used methods for water demand forecasting are based on univariate time series models, such as autoregressive moving average (ARMA) models (Hutton and Kapelan 2015b). In order to take into account exogenous processes, (multiple) (non-)linear regression or exogenous ARMA models were used, under the condition that extensive data on each of these exogenous processes are available (Adamowski et al 2012; Papageorgiou et al 2015; Froelich 2016; Candelieri 2017)

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Conclusion