Gu, Jianli, authorLiu, Jiangguo, advisorCarlson, Kenneth H., committee memberZhou, Yongcheng, committee member2017-01-042017-01-042016http://hdl.handle.net/10217/178928Public concerns about groundwater quality have increased in recent years due to the massive exploitation of shale gas through hydraulic fracturing which raises the risk of groundwater contamination. Groundwater monitoring can fill the gap between the public fears and the industrial production. However, the studies of groundwater anomaly detection are still insufficient. The complicated sequential data patterns generated from subsurface water environment bring many challenges that need comprehensive modeling techniques in mathematics, statistics and machine learning for effective solutions. In this reseach, Multivariate State Estimation Technique (MSET) and One-class Support Vector Machine (1-SVM) methods are utilized and improved for real-time groundwater anomaly detection. The effectiveness of the two methods are validated based upon different data patterns coming from the historic data of Colorado Water Watch (CWW) program. Meanwhile, to ensure the real-time responsiveness of these methods, a groundwater event with contaminant transport was simulated by means of finite difference methods (FDMs). The numerical results indicate the change of contaminant concentration of chloride with groundwater flow over time. By coupling the transport simulation and groundwater monitoring, the reliability of these methods for detecting groundwater contamination event is tested. This research resolves issues encountered when conducting real-time groundwater monitoring, and the implementation of these methods based on Python can be easily transfered and extended to engineering practices.born digitalmasters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.Text