Machine learning methods for alarm detection and prediction

用于警报检测和预测的机器学习方法

• 批准号: 535755-2018
• 负责人: McLeod, Robert
• 金额: $ 1.81万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=659379
• 关键词: Machine; learning; methods; alarm; detection

Water quality monitoring is becoming increasingly important. The importance of tracking changes in water quality can't be overstated: human health and livelihoods depend on clean, reliable water supplies. Remote real time water quality monitoring systems specializing in drinking water, surface water, ground water, industrial water and waste water are becoming more feasible and economical than ever. These systems are some of the first to integrate Internet of Things (IoT) technologies and remote sensing oriented to health and safety. As with all advances, being early adopters in integrating sensors, systems and software on scale presents considerable research questions and challenges. Of particular concern in this Engage Grant opportunity, is how machine learning can be best leveraged to help predict alarms from real time water quality data while mitigating against false alarms. The data is inherently temporal and presents challenges that interpreting spatial data does not involve. Although machine learning has made astonishing progress in image recognition and in learning strategies or policies in games such as chess or go, time series data is still of the most difficult to analyze. This research project will evaluate machine learning alternatives and ensemble methodolgies (such as those based on support vector machines, convolutional neural networks, recurrent neural networks as well as more traditional neural networks) with the objective to determine the most suitable machine learning approach to improve the prediction of water quality alarm states as well as the mitigation of false positives.********

水质监测变得越来越重要。跟踪水质变化的重要性怎么强调都不为过：人类健康和生计取决于清洁、可靠的供水。 专门针对饮用水、地表水、地下水、工业用水和废水的远程实时水质监测系统变得比以往更加可行和经济。这些系统是首批集成物联网 (IoT) 技术和面向健康和安全的遥感技术的系统。与所有进步一样，成为大规模集成传感器、系统和软件的早期采用者面临着相当大的研究问题和挑战。在这个参与资助机会中特别令人关注的是如何最好地利用机器学习来帮助根据实时水质数据预测警报，同时减少误报。数据本质上是时间性的，并带来了解释空间数据所不涉及的挑战。尽管机器学习在图像识别以及国际象棋或围棋等游戏中的学习策略或策略方面取得了惊人的进步，但时间序列数据仍然是最难分析的。该研究项目将评估机器学习替代方案和集成方法（例如基于支持向量机、卷积神经网络、循环神经网络以及更传统的神经网络的方法），目的是确定最合适的机器学习方法来改进水质警报状态的预测并减少误报。********