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Heat wave hazard classification and risk assessment using artificial intelligence fuzzy logic

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Abstract

The average summer temperatures as well as the frequency and intensity of hot days and heat waves are expected to increase due to climate change. Motivated by this consequence, we propose a methodology to evaluate the monthly heat wave hazard and risk and its spatial distribution within large cities. A simple urban climate model with assimilated satellite-derived land surface temperature images was used to generate a historic database of urban air temperature fields. Heat wave hazard was then estimated from the analysis of these hourly air temperatures distributed at a 1-km grid over Athens, Greece, by identifying the areas that are more likely to suffer higher temperatures in the case of a heat wave event. Innovation lies in the artificial intelligence fuzzy logic model that was used to classify the heat waves from mild to extreme by taking into consideration their duration, intensity and time of occurrence. The monthly hazard was subsequently estimated as the cumulative effect from the individual heat waves that occurred at each grid cell during a month. Finally, monthly heat wave risk maps were produced integrating geospatial information on the population vulnerability to heat waves calculated from socio-economic variables.

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Acknowledgements

The authors thank Dr. Dimitra Founda from the Institute of Environmental Research and Sustainable Development of NOA for providing the station dataset and Ms. Maria Mihelaraki from the Hellenic National Meteorological Service for her contribution regarding heat waves in Athens. Census block data were provided by the Hellenic Statistical Authority (www.statistics.gr). The work was funded by the European Space Agency project ‘Urban Heat Islands and Urban Thermography’ (www.urbanheatisland.info; Grant no. 21913/08/I-LG). The authors wish to acknowledge the input from the anonymous reviewers, which substantially improved the manuscript.

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Authors and Affiliations

  1. Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens, Metaxa & Vassileos Pavlou Str, GR 152 36, Palea Penteli, Athens, Greece

    Iphigenia Keramitsoglou & Ioannis A. Daglis

  2. School of Chemical Engineering, National Technical University of Athens, Zografou Campus, GR 157 80, Athens, Greece

    Chris T. Kiranoudis

  3. VITO Flemish Institute for Technological Research, Boeretang 200, B-2400, Mol, Belgium

    Bino Maiheu & Koen De Ridder

  4. Planetek Italia S.r.l., via Massaua, 12-70132, Bari, Italy

    Paolo Manunta

  5. European Space Agency, ESRIN, Via Galileo Galilei, Casella Postale 64 I-00044, Frascati, Italy

    Marc Paganini

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  1. Iphigenia Keramitsoglou
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  2. Chris T. Kiranoudis
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Correspondence to Iphigenia Keramitsoglou.

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Keramitsoglou, I., Kiranoudis, C.T., Maiheu, B. et al. Heat wave hazard classification and risk assessment using artificial intelligence fuzzy logic. Environ Monit Assess 185, 8239–8258 (2013). https://doi.org/10.1007/s10661-013-3170-y

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