Journal
ENVIRONMENTAL MODELLING & SOFTWARE
Volume 25, Issue 9, Pages 1014-1022Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.envsoft.2009.08.010
Keywords
Coastal environment; Data-driven modeling; Anomaly detection; Machine learning; Real-time data; Sensor networks; Data quality control; Artificial intelligence
Categories
Funding
- Office of Naval Research [N00014-04-1-0437]
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The deployment of environmental sensors has generated an interest in real-time applications of the data they collect. This research develops a real-time anomaly detection method for environmental data streams that can be used to identify data that deviate from historical patterns. The method is based on an autoregressive data-driven model of the data stream and its corresponding prediction interval. It performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no pre-classification of anomalies. Furthermore, this method can be easily deployed on a large heterogeneous sensor network. Sixteen instantiations of this method are compared based on their ability to identify measurement errors in a windspeed data stream from Corpus Christi, Texas. The results indicate that a multilayer perceptron model of the data stream, coupled with replacement of anomalous data points, performs well at identifying erroneous data in this data stream. (C) 2009 Published by Elsevier Ltd.
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