Detection of Anomalous Pollution Sensors Using Deep Learning Strategies

B. M. Peralta, R. Soria, S. Berres, L. Caro, A. Mellado, N. Schiappacasse

Research output: Contribution to journalConference articlepeer-review

Abstract

In recent years, the pollution problem has gained great importance due to its socioeconomic implications for people regarding health or logistic issues. The pollution level classically is measured with specialized expensive detectors located in some few locations. In the case of Temuco city there are three such centralized pollution monitoring stations. An alternative approach for measuring the pollution level of cities makes use of inexpensive pollution sensors located on public transportation vehicles. Nonetheless, a drawback of this approach is that these inexpensive sensors can be sensitive to noise, vehicle movement, human intervention or technical failures. Therefore, it is relevant to be able to automatically detect inaccurate or failing sensors as they are multiple and cannot be submitted frequently to a technical revision. In this work, we propose a method to automatically detect these anomalous sensors by an unsupervised deep learning approach using autoencoders. This work is part of an ongoing project where massive data are not still available. In this context, the simulated output of mobile pollution sensors is generated by a time series model that systematically inserts outlier measurements. Our results indicate that the proposed detection method is able to reliably reproduce the data generated and to detect the simulated outliers with an accuracy of over 95%.

Original languageEnglish
Article number012032
JournalIOP Conference Series: Earth and Environmental Science
Volume503
Issue number1
DOIs
Publication statusPublished - 18 Jun 2020
EventSustainable Built Environment Temuco 2019, SBE 2019 Temuco - Temuco, Chile
Duration: 16 Oct 201918 Oct 2019

ASJC Scopus subject areas

  • Environmental Science(all)
  • Earth and Planetary Sciences(all)

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