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Environ Monit Assess. 2013 Oct;185(10):8239-58. doi: 10.1007/s10661-013-3170-y. Epub 2013 Apr 27.

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

Author information

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. ik@noa.gr

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.

PMID:
23625352
DOI:
10.1007/s10661-013-3170-y
[Indexed for MEDLINE]
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