Developing Ensemble Methods for Detecting Anomalies in Water Level Data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Telemetry is an automatic system for monitoring environments in a remote or inaccessible area and transmitting data via various media. Data from telemetry stations can be used to produce early warning or decision supports in risky situations. However, sometimes a device in a telemetry system may not work properly and generates some errors in the data, which lead to false alarms or miss true alarms for disasters. We then developed two types of ensembles: (1) simple and (2) complex ensembles for automatically detecting the anomaly data. The ensembles were tested on the data collected from 9 telemetry water level stations and the results clearly show that the complex ensembles are the most accurate and also reliable in detecting anomalies.

Original languageEnglish
Title of host publicationThe 22nd International Conference on Big Data Analytics and Knowledge Discovery
EditorsMax Bramer, Richard Ellis
PublisherSpringer
Pages145-151
Number of pages7
ISBN (Print)9783030637989
DOIs
Publication statusPublished - 8 Dec 2020
EventThe 22nd International Conference on Big Data Analytics and Knowledge Discovery - Bratislava, Bratislava, Slovakia
Duration: 14 Sep 202017 Sep 2020
http://www.dexa.org/dawak2020

Conference

ConferenceThe 22nd International Conference on Big Data Analytics and Knowledge Discovery
CountrySlovakia
CityBratislava
Period14/09/2017/09/20
Internet address

Keywords

  • Anomaly detection
  • Ensemble methods
  • Water level telemetry monitoring

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