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

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.

Automated skin lesion assessment using mobile technologies and cloud platforms.

This paper presents a smart phone based system for storing digital images of skin areas depicting regions of interest (lesions) and performing self-assessment of these skin lesions within these areas. The system consists of a mobile application that can acquire and identify moles in skin images and classify them according their severity into melanoma, nevus and benign lesions. The proposed system includes also a cloud infrastructure exploiting computational and storage resources. This cloud-based architecture provides interoperability and support of various mobile environments as well as flexibility in enhancing the classification model. Initial evaluation results are quite promising and indicate that the application can be used for the task of skin lesions initial assessment.

Multi-objective evolutionary emergency response optimization for major accidents.

Emergency response planning in case of a major accident (hazardous material event, nuclear accident) is very important for the protection of the public and workers' safety and health. In this context, several protective actions can be performed, such as, evacuation of an area; protection of the population in buildings; and use of personal protective equipment. The best solution is not unique when multiple criteria are taken into consideration (e.g. health consequences, social disruption, economic cost). This paper presents a methodology for multi-objective optimization of emergency response planning in case of a major accident. The emergency policy with regards to protective actions to be implemented is optimized. An evolutionary algorithm has been used as the optimization tool. Case studies demonstrating the methodology and its application in emergency response decision-making in case of accidents related to hazardous materials installations are presented. However, the methodology with appropriate modification is suitable for supporting decisions in assessing emergency response procedures in other cases (nuclear accidents, transportation of hazardous materials) or for land-use planning issues.

A multidisciplinary decision support system for forest fire crisis management.

A wildland fire is a serious threat for forest ecosystems in Southern Europe affecting severely and irreversibly regions of significant ecological value as well as human communities. To support decision makers during large-scale forest fire incidents, a multidisciplinary system has been developed that provides rational and quantitative information based on the site-specific circumstances and the possible consequences. The system's architecture consists of several distinct supplementary modules of near real-time satellite monitoring and fire forecast using an integrated framework of satellite Remote Sensing, GIS, and RDBMS technologies equipped with interactive communication capabilities. The system may handle multiple fire ignitions and support decisions regarding dispatching of utilities, equipment, and personnel that would appropriately attack the fire front. The operational system was developed for the region of Penteli Mountain in Attika, Greece, one of the mountain areas in the country most hit by fires. Starting from a real fire incident in August 2000, a scenario is presented to illustrate the effectiveness of the proposed approach.