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

Area of Science:

• Urban climatology
• Environmental science
• Artificial intelligence applications

Background:

• Climate change is increasing summer temperatures, heat waves, and hot days.
• Urban areas face amplified heat wave impacts due to the urban heat island effect.
• Assessing heat wave hazard and risk is crucial for urban adaptation strategies.

Purpose of the Study:

• To develop and apply a methodology for evaluating monthly heat wave hazard and risk in large cities.
• To map the spatial distribution of heat wave hazard and risk.
• To integrate population vulnerability into heat wave risk assessments.

Main Methods:

• Utilized an urban climate model with assimilated satellite-derived land surface temperature data to create historical urban air temperature fields.
• Employed an artificial intelligence fuzzy logic model to classify heat waves (mild to extreme) based on duration, intensity, and timing.
• Calculated monthly heat wave hazard as the cumulative effect of individual heat wave events per grid cell.
• Integrated geospatial population vulnerability data derived from socio-economic variables to produce risk maps.

Main Results:

• Generated hourly air temperature data at a 1-km grid resolution for Athens, Greece.
• Identified specific urban areas with higher susceptibility to extreme temperatures during heat waves.
• Developed monthly heat wave hazard and risk maps, highlighting vulnerable populations.

Conclusions:

• The proposed methodology effectively assesses and maps monthly heat wave hazard and risk in urban environments.
• The AI-driven approach provides a nuanced classification of heat wave severity.
• Integrating socio-economic vulnerability data is essential for targeted urban heat wave risk management.