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.

饮用水管爆裂不仅会导致饮用水损失，而且还会破坏地下基础设施和地面基础设施。短期需水量预测是爆破检测中的重要工具，因为预测和实际需水量之间的偏差可能表明出现了新的爆裂。由于诸如由于天气，假日，节日或流行病等环境，经济或人口统计学的外在影响而导致的非季节性用水，许多爆破检测方法都难以解决误报问题。找到一个可靠的替代方案以降低突发检测的误报率，并且不依赖于来自外部过程的数据，这一点至关重要。我们提出了一种基于贝叶斯岭回归和随机样本共识的突发检测方法。我们的外生临近预报方法依赖于配电网中所有附近流量和压力传感器的信号，目的是减少误报率。该方法既不需要外部过程的数据，也不需要大量的历史数据，但是每个流量/压力传感器只需要一个星期的历史数据。将外源临近预报方法与用于爆破检测的常用需水量预测方法进行了比较，显示出Nash-Sutcliffe模型的效率分别为57.9％-77.7％和82.7％-90.6％。这些效率范围指示更准确的需水量预测，从而导致更精确的突发检测。但每个流量/压力传感器仅需要一周的历史数据。将外源临近预报方法与用于爆破检测的常用需水量预测方法进行了比较，显示出Nash-Sutcliffe模型的效率分别为57.9％-77.7％和82.7％-90.6％。这些效率范围指示更准确的需水量预测，从而导致更精确的突发检测。但每个流量/压力传感器仅需要一周的历史数据。将外源临近预报方法与用于爆破检测的常用需水量预测方法进行了比较，显示出Nash-Sutcliffe模型的效率分别为57.9％-77.7％和82.7％-90.6％。这些效率范围指示更准确的需水量预测，从而导致更精确的突发检测。