An Automated Anomaly Detection Procedure for Hourly Observed Precipitation in Near-Real Time Application

Sheng Chi Yang*, Ming Chang Wu, Hong Ming Kao, Tsun Hua Yang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Scopus citations


For setting up an early warning system or for making a decision of disaster mitigation, real-time precipitation observation collected by in situ stations are necessary. However, the data easily become corrupted because of sensor failure or communication error. The simulations of a relevant hydrologic model under an extreme situation could be ridiculous due to incorrect precipitation observations as model inputs. Anomaly detection approaches are essential to flag anomalous data that not conform to expected behavior from normal data in near-real time. Manual inspection was used as an anomaly detection approach, but no longer applicable for big data and near-real time application due to manpower limitation. Therefore, this study proposes an automated anomaly detection procedure for precipitation including neighboring station selection and spatial consistency checking. First, the neighboring stations are not manually selected by a fixed distance, but automatically selected by the self-organizing maps (SOM) with recent historical records. Second, the target observation pairwise compares against concurrent neighboring observations to flag inconsistent data as anomalies. Because a suitable estimation of the target station is unnecessary in this procedure, it is applicable in areas with high spatial and temporal variability.

Original languageEnglish
Title of host publicationSpringer Water
PublisherSpringer Nature
Number of pages7
StatePublished - 2020

Publication series

NameSpringer Water
ISSN (Print)2364-6934
ISSN (Electronic)2364-8198


  • Anomaly detection
  • Classifier
  • Machine learning
  • Rain gauges
  • Self-organizing map


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