The focus of this research is to develop an event detection system for groundwater flooding. The groundwater monitoring infrastructure is based on a Wireless Sensor Network, which can often lead to corrupted data values. The event detection system seeks to develop an algorithm to identify these corrupted data values and flag them as a false event to the user. It aims to maintain a high accuracy of true events, while keeping the occurrence of a false event to a minimum. The algorithm is based on threshold classification technique to identify the event and a statistical modified Z Score calculation to determine if the event is true or false. I ran the algorithm with an historic flood event and measured the accuracy of the results with a ROC curve. Results showed a 95% accuracy level but it also identified 8 out of 160 true events as false, which leads me to recommend a further analysis into the threshold that defined the algorithm. The technique would also need to be tested on larger dataset to validate this accuracy.