Machine Learning Methods for Monitoring of Complex Water and Sewer Network Infrastructure

该项目的目的是开发数据驱动的传感器放置和状态估计方法，预测水系统问题的鲁棒性和可靠性的理论保证。该项目的结构沿着以下目标：1.制定最佳的传感器布局策略，最大限度地提高传感基础设施获取的信息量.设计并验证数据驱动的状态估计和故障检测算法，将传感器网络中的信息增加到水动力学模型中.设计和验证机器学习预测工具，预测网络状态和关键性能指标，例如故障事件、阻塞、溢出、泄漏等的发生。

The aim of this project is to develop data-driven sensor placement and state estimation methods that predict water system problems with theoretical guarantees of robustness and reliability. The project is structured along the following objectives:1. Develop optimal sensor placement strategies that maximize the amount ofinformation acquired by the sensing infrastructure.2. Design and validate data-driven state estimation and fault detection algorithms thataugment the information from the sensor network to the hydrodynamic models.3. Design and validate machine learning forecasting tools that predict the state of thenetwork and key performance metrics, such as the occurrence of fault events,blockages, overflows, leaks etc.