论文标题
SAM-KNN回归剂用于水分配网络中的在线学习
SAM-kNN Regressor for Online Learning in Water Distribution Networks
论文作者
论文摘要
供水网络是住房和工业现代基础设施的关键组成部分。他们通过广泛的分支网络从来源运输和分配水。为了始终保证工作网络，供水公司不断监视网络并在必要时采取行动 - 例如对泄漏，传感器故障和水质下降的反应。由于现实世界网络太大且复杂而无法由人类监测，因此已经开发了算法监测系统。此类系统的流行类型是基于残差的异常检测系统，可以检测泄漏和传感器故障等事件。对于连续的高质量监控，这些系统有必要适应各种异常的需求和存在。 在这项工作中，我们提出了对回归的增量SAM-KNN分类器的改编，以构建一个基于剩余的异常检测系统，以适应水分配网络，以适应任何类型的变化。
Water distribution networks are a key component of modern infrastructure for housing and industry. They transport and distribute water via widely branched networks from sources to consumers. In order to guarantee a working network at all times, the water supply company continuously monitors the network and takes actions when necessary -- e.g. reacting to leakages, sensor faults and drops in water quality. Since real world networks are too large and complex to be monitored by a human, algorithmic monitoring systems have been developed. A popular type of such systems are residual based anomaly detection systems that can detect events such as leakages and sensor faults. For a continuous high quality monitoring, it is necessary for these systems to adapt to changed demands and presence of various anomalies. In this work, we propose an adaption of the incremental SAM-kNN classifier for regression to build a residual based anomaly detection system for water distribution networks that is able to adapt to any kind of change.
