Journal article

The good, the bad and the outliers: automated detection of errors and outliers from groundwater hydrographs

Tim J Peterson, Andrew W Western, Xiang Cheng

Hydrogeology Journal | SPRINGER | Published : 2018

Abstract

Suspicious groundwater-level observations are common and can arise for many reasons ranging from an unforeseen biophysical process to bore failure and data management errors. Unforeseen observations may provide valuable insights that challenge existing expectations and can be deemed outliers, while monitoring and data handling failures can be deemed errors, and, if ignored, may compromise trend analysis and groundwater model calibration. Ideally, outliers and errors should be identified but to date this has been a subjective process that is not reproducible and is inefficient. This paper presents an approach to objectively and efficiently identify multiple types of errors and outliers. The a..

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Grants

Awarded by Australian Research Council


Funding Acknowledgements

This research was funded by the Australian Research Council Linkage Project LP130100958 and funding partners: Bureau of Meteorology (Australia); Department of Environment, Land, Water and Planning (Vic., Australia); Department of Economic Development, Jobs, Transport and Resources (Vic., Australia); and Power and Water Corporation (N.T., Australia). The authors are grateful to Dr. Elisabetta Carrara (Bureau of Meteorology) for her constructive input during the development of the algorithms.