Fusing Data Analytics with Hydraulics in a Hydroinformatics Approach for Water Distribution System Monitoring

该项目将开发一种新的方案，通过低成本地结合各种测量，从单个房屋使用的实时计量到系统流量和压力状况，提供从单个家庭到整个供水系统（WDS）的全面监测。 监测系统将提供随时间推移的整个白龙会水力条件的预测。以智能方式将这些预测与现场测量能力进行比较将提高突发检测准确性，减少检测时间并提高定位突发的准确性，从而缩短维修时间，降低成本和损害。 本项目中的现场演示和方法验证将演示爆破检测/定位的主动方法。 该项目将促进跨学科的研究生和本科生的教育和培训，提高研究机会，促进女性和代表性不足的少数群体，并吸引K-12学生到工程programmes.The项目的目标是开发新一代的白龙会监测系统在一个新的跨学科的水文信息学范式，通过融合水力知识与数据驱动的时空分析。 实时爆破检测算法将采用似然比检验统计量，结合多变量控制图比较预测和测量的压力和流量，以最大限度地提高事件检测率并避免误报。 在确定发生突发之后，该方法返回到WDS水力学，以估计对各个位置处的突发的响应，并将测量偏差与预测条件相关联。 鉴于实施成本相对较低，本研究中开发的概念和技术将在相当大的国际市场上具有巨大的商业潜力。该项目将为水务公司展示一个平台，以快速吸收新的传感技术，并获得更广泛的利益，因为将这种扩展的数据资源纳入白龙会控制系统的机会是明确的。时空传感器融合机制将增强数据分析，以解决其他复杂的非静态系统中的问题，其中实时建模，监控和决策是感兴趣的。 综合系统和土木工程研究合作以及通过实地研究与实践的联系为建立跨学科教育计划，研究生和本科生的多学科培训以及学生研究机会提供了独特的机会。通过维护数据和案例库，在会议和期刊上发表论文，以及与行业合作伙伴合作，确保广泛传播。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project will develop a novel scheme that seeks to provide full-scale monitoring from individual households to the entire water distribution system (WDS) by inexpensively combining various kinds of measurements from real-time metering of individual premise use to system flow and pressure conditions over time. The monitoring system will provide predictions on hydraulic conditions across the WDS over time. Comparing these predictions with field measurements capabilities in smart ways will increase burst detection accuracy, reduce detection time and improve accuracy of locating bursts resulting in shorter repair times, and lower costs and damages. Field demonstration and validation of the approaches within this project will demonstrate the active approach to burst detection/location. The project will promote inter-disciplinary graduate and undergraduate education and training, enhance research opportunities that promote female and underrepresented minority groups, and attract K-12 students to engineering programs.The objective of this project is to develop a new generation of WDS monitoring systems within a new interdisciplinary hydroinformatics paradigm by fusing hydraulic knowledge with data-driven spatio-temporal analytics. The real-time burst detection algorithm will employ likelihood ratio test statistics to compare predicted and measured pressures and flows in conjunction with multivariate control charts to maximize event detection rates and avoid false alarms. After a burst is determined to be occurring, the approach returns to the WDS hydraulics to estimate responses to bursts at various locations and correlate measurement deviations to predicted conditions. Given the relatively low implementation costs, the concepts and technologies developed in this research will have significant commercial potential in a sizable international market. The project will demonstrate a platform for water utilities to rapidly assimilate new sensing technology and reap broader benefits as opportunities to incorporate this expanded data resources in WDS control systems are clear. The spatio-temporal sensor fusion mechanism will enhance data analytics for solving problems in other complex non-stationary systems where real-time modeling, monitoring and decision-making are of interest. The integrated systems and civil engineering research collaboration and the link to practice through field studies offer unique opportunities for establishing an inter-disciplinary education program, for multidisciplinary training of graduate and undergraduate students, and for student research opportunities. Broad dissemination will be ensured by maintaining data and case libraries, publishing papers in conferences and refereed journals, and collaborating with industry partners.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.