The identification of flood-prone areas is a fundamental component of rational urban planning and proper natural disaster management policy. The aim of the present study is to introduce a framework for the identification of flood-prone areas using geographical information systems techniques and decision-making, based on a comparative evaluation for various scenarios. As a case study, the Attica region in Greece is selected, which is occasionally affected by heavy rainfall, the main cause of flooding in the region, coupled with the fact that human activities and urbanization of recent years play a significant role in flood occurrence. In this context, the development and application of a GIS-based multi-criteria analysis method for the determination of areas susceptible to flood events is initially presented. The entire spatial analysis is performed using SAGA 6.3.0 and ArcMap 10.2 Desktop, by applying a number of alternative modifications and, finally, by evaluating different scenarios regarding methods for the criteria standardization, criteria hierarchy and factors' weighting estimation. The proposed framework has an advantage among other approaches, since it takes into account mainly static data that are linked to flooding, such as the topography and land cover distribution and it can be easily customized in ungauged catchments. K E Y W O R D Sflood vulnerability mapping, flood-prone areas, fuzzy, GIS, k-means, multi-criteria analysis, ungauged catchments

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Five different water resource management scenarios are examined on eight dry islands of the Aegean Sea in Greece, pitting the current practice of water hauling via ship against alternative water supply schemes in delivering a sustainable solution for meeting water demand. The first scenario employs current water supply practices along with the operation of domestic rainwater harvesting systems. Desalinated water, provided through the operation of wind-powered desalination plants, is considered the main source of potable water in the rest of scenarios. Wind-powered desalination may be combined with rainwater harvesting as a supplementary source of water and/or seawater pumping and an additional source of energy that is supplied to the system. All different alternatives are evaluated for a 30-year lifespan, and an optimal solution is proposed for each island, based on a life cycle cost (LCC) analysis. The performance of this solution is then assessed under six climate change (CC) scenarios in terms of the rate of on-grid versus off-grid renewable energy that is required in order to achieve a certain reliability level. Overall, the examined scenarios show a decreasing performance in terms of reliability under CC for the eight islands.

In this article, geovisualization is used for the presentation and interpretation of spatial analysis results concerning several house attributes. For that purpose, point data for houses in the region of Attica, Greece are analyzed. The data concern houses for sale and comprise structural characteristics, such as size, age and floor, as well as locational attributes. Geovisualization of house characteristics is performed employing spatial interpolation techniques, kriging techniques, in particular. Spatial autocorrelation in the data is examined through the calculation of the Moran’s I coefficient, while spatial clusters of houses with similar characteristics are identified using the Getis-Ord Gi* local spatial autocorrelation coefficient. Finally, a model is developed in order to predict house prices according to several structural and locational characteristics. In that respect, a classic hedonic pricing model is constructed, which is consequently developed as a geographically weighted regression (GWR) model in a GIS environment. The results of this model indicate that two characteristics, i.e., size and age, account for most of the variability in house prices in the study region. Since GWR is a local model producing different regression parameters for each observation, it is possible to obtain the spatial distribution of the regression parameters, which indicate the significance of the house characteristics for price determination in different locations in the study area.