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Extreme anomaly event detection in biosphere using linear regression and a spatiotemporal MRF model

Author

Listed:
  • Yanira Guanche García

    (Friedrich Schiller University Jena
    Michael Stifel Center for Data-Driven and Simulation Science Jena)

  • Maha Shadaydeh

    (Friedrich Schiller University Jena)

  • Miguel Mahecha

    (Michael Stifel Center for Data-Driven and Simulation Science Jena
    Max Planck Institute for Biogeochemistry)

  • Joachim Denzler

    (Friedrich Schiller University Jena
    Michael Stifel Center for Data-Driven and Simulation Science Jena)

Abstract

Detecting abnormal events within time series is crucial for analyzing and understanding the dynamics of the system in many research areas. In this paper, we propose a methodology to detect these anomalies in multivariate environmental data. Five biosphere variables from a preliminary version of the Earth System Data Cube have been used in this study: Gross Primary Productivity, Latent Energy, Net Ecosystem Exchange, Sensible Heat and Terrestrial Ecosystem Respiration. To tackle the spatiotemporal dependencies of the biosphere variables, the proposed methodology after preprocessing the data is divided into two steps: a feature extraction step applied to each time series in the grid independently, followed by a spatiotemporal event detection step applied to the obtained novelty scores over the entire study area. The first step is based on the assumption that the time series of each variable can be represented by an autoregressive moving average (ARMA) process, and the anomalies are those time instances that are not well represented by the estimated ARMA model. The Mahalanobis distance of the ARMA models’ multivariate residuals is used as a novelty score. In the second step, the obtained novelty scores of the entire study are treated as time series of images. Markov random fields (MRFs) provide an effective and theoretically well-established methodology for integrating spatiotemporal dependency into the classification of image time series. In this study, the classification of the novelty score images into three classes, intense anomaly, possible anomaly, and normal, is performed using unsupervised K-means clustering followed by multi-temporal MRF segmentation applied recursively on the images of each consecutive $$L \ge $$ L ≥ 1 time steps. The proposed methodology was applied to an area covering Europe and Africa. Experimental results and validation based on known historic events show that the method is able to detect historic events and also provides a useful tool to define sensitive regions.

Suggested Citation

  • Yanira Guanche García & Maha Shadaydeh & Miguel Mahecha & Joachim Denzler, 2019. "Extreme anomaly event detection in biosphere using linear regression and a spatiotemporal MRF model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 98(3), pages 849-867, September.
  • Handle: RePEc:spr:nathaz:v:98:y:2019:i:3:d:10.1007_s11069-018-3415-8
    DOI: 10.1007/s11069-018-3415-8
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    References listed on IDEAS

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    1. Jakob Zscheischler & Seth Westra & Bart J. J. M. Hurk & Sonia I. Seneviratne & Philip J. Ward & Andy Pitman & Amir AghaKouchak & David N. Bresch & Michael Leonard & Thomas Wahl & Xuebin Zhang, 2018. "Future climate risk from compound events," Nature Climate Change, Nature, vol. 8(6), pages 469-477, June.
    2. Markus Reichstein & Michael Bahn & Philippe Ciais & Dorothea Frank & Miguel D. Mahecha & Sonia I. Seneviratne & Jakob Zscheischler & Christian Beer & Nina Buchmann & David C. Frank & Dario Papale & An, 2013. "Climate extremes and the carbon cycle," Nature, Nature, vol. 500(7462), pages 287-295, August.
    3. du Preez, Johann & Witt, Stephen F., 2003. "Univariate versus multivariate time series forecasting: an application to international tourism demand," International Journal of Forecasting, Elsevier, vol. 19(3), pages 435-451.
    4. Yuzhi Cai, 2011. "Multi‐variate time‐series simulation," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(5), pages 566-579, September.
    5. Dim Coumou & Stefan Rahmstorf, 2012. "A decade of weather extremes," Nature Climate Change, Nature, vol. 2(7), pages 491-496, July.
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