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Volcanic Anomalies Detection Through Recursive Density Estimation

  • Jose Eduardo Gomez
  • David Camilo Corrales
  • Emmanuel LassoEmail author
  • Jose Antonio Iglesias
  • Juan Carlos Corrales
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11288)

Abstract

The volcanic conditions of Latin America and the Caribbean propitiate the occurrence of natural disaster in these areas. The volcanic-related disasters alter the living conditions of the populations compromised by their activity. We propose to use Recursive Density Estimation (RDE) method to detect volcanic anomalies. The different data used for the design and evaluation of this method are obtained from Puraće volcano of two surveillance volcanic areas: Geochemistry and Deformation. The proposed method learns quickly from data streams in real time and the different volcanic anomalies can be detected taking into account all the previous data of the volcano. RDE achieves good performance in the outliers detection; 82% of precision for geochemestry data, while 77% of precision in geodesy data.

Keywords

Recursive Density Estimation (RDE) Geochemistry Deformation Outlier 

Notes

Acknowledgments

We are grateful to the Colombian Geological Survey (SGC) - especially to the volcanological and seismological observatory located in Popayán (OVSPOP) - for giving us the necessary data and helping us with this paper. In addition, we are grateful to Colciencias (Colombia) for PhD scholarship granted to MsC. David Camilo Corrales. This work has been also supported by:

– Project: “Alternativas Innovadoras de Agricultura Inteligente para sistemas productivos agrícolas del departamento del Cauca soportado en entornos de IoT - ID 4633” financed by Convocatoria 04C–2018 “Banco de Proyectos Conjuntos UEES-Sostenibilidad” of Project “Red de formación de talento humano para la innovación social y productiva en el Departamento del Cauca InnovAcción Cauca”.

– The Spanish Ministry of Economy, Industry and Competitiveness (Projects TRA2015-63708-R and TRA2016-78886-C3-1-R).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jose Eduardo Gomez
    • 1
    • 2
  • David Camilo Corrales
    • 2
    • 3
  • Emmanuel Lasso
    • 2
    Email author
  • Jose Antonio Iglesias
    • 3
  • Juan Carlos Corrales
    • 2
  1. 1.Observatorio Vulcanológico y SismológicoPopayánColombia
  2. 2.Telematics Engineering GroupUniversity of CaucaPopayánColombia
  3. 3.Computer Science DepartmentCarlos III University of MadridLeganesSpain

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