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Environmental extreme events detection: A survey

10 pagesPublished: September 27, 2019

Abstract

The application of statistics in extreme events detection is quite diverse and leads to diverse formulations, which needs to be designed for the specific problem. Each formula needs to be tailored specifically to work with the available data in the given situation. This diversity is one of the driving forces of this survey towards identifying the most common mixture of components utilized in the analysis of environmental outlier detection. Indeed for some arbitrary applications, it may not always be possible to use off-the-shelf models due to the wide variations in problem formulations. In this paper, we summarize the statistical methods involved in the detection of environmental extremes such as wind ramps, high precipitation and extreme temperatures. Then we organize the discussion along different outlier detection types, present various outlier definitions, and briefly introduce the corresponding techniques. Environmental extreme events detection challenges and possible future work are also discussed.

Keyphrases: dynamic programming, Extreme Value Theory, Outliers, sliding window, threshold

In: Frederick C. Harris Jr, Sergiu Dascalu, Sharad Sharma and Rui Wu (editors). Proceedings of 28th International Conference on Software Engineering and Data Engineering, vol 64, pages 184--193

Links:
BibTeX entry
@inproceedings{SEDE2019:Environmental_extreme_events_detection,
  author    = {Rakesh Matta and Rui Wu and Shanyue Guan},
  title     = {Environmental extreme events detection: A survey},
  booktitle = {Proceedings of 28th International Conference on Software Engineering and Data Engineering},
  editor    = {Frederick Harris and Sergiu Dascalu and Sharad Sharma and Rui Wu},
  series    = {EPiC Series in Computing},
  volume    = {64},
  pages     = {184--193},
  year      = {2019},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/mB1q},
  doi       = {10.29007/jkzt}}
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