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A data mining approach to evaluate suitability of dissolved oxygen sensor observations for lake metabolism analysis

Despite rapid growth in continuous monitoring of dissolved oxygen for lake metabolism studies, the current best practice still relies on visual assessment and manual data filtering of sensor observations by experienced scientists in order to achieve meaningful results. This time consuming approach is fraught with potential for inconsistency and individual subjectivity. An automated method to assure the quality of data for the purpose of metabolism modeling is clearly needed to obtain consistent results representative of collective expertise. We used a hybrid approach of expert panel and data mining for data filtration. Symbolic Aggregate approXimation (SAX) treats discretized numerical timeseries segments as symbolic indications, creating a series of strings which are literally comparable to human words and sentences. This conversion allows established text mining techniques, such as classification methods to be applied to timeseries data. Half‐hourly frequency surface dissolved oxygen data from 18 global lakes were used to create day‐long segments of the original time series data. Three hundred sets of 1‐d measurements were provided to a group of seven anonymous experts, experienced in manual filtering of oxygen data for metabolism modeling studies. The collective results were treated as expert panel decisions, and were used to rank the data by confidence level for use in metabolism calculations. While considerable variation occurred in the way the experts perceived the quality of the data, the model provides an objective and quantitative assessment method. The program output will assist the decision making process in determining whether data should be used for metabolism calculations. An R version of the program is available for download.
Journal Article
Type of thesis
Muraoka, K., Hanson, P., Frank, E., Jiang, M., Chiu, K., & Hamilton, D. P. (2018). A data mining approach to evaluate suitability of dissolved oxygen sensor observations for lake metabolism analysis. Limnology and Oceanography: Methods, published online on 04 October 2018. https://doi.org/10.1002/lom3.10283
© 2018 The Authors. Limnology and Oceanography: Methods published by Wiley Periodicals, Inc. on behalf of Association for the Sciences of Limnology and Oceanography. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.