| Abstract | The present work introduces a national scale flood hazard assessment methodology, using multi-criteria analysis and artificial neural networks (ANNs) techniques in a GIS environment. The proposed methodology was applied in Greece, where flash floods are a relatively frequent phenomenon and it has become more intense over the last decades, causing significant damages in rural and urban sectors. In order the most prone flooding areas to be identified, seven factor-maps (that are directly related to flood generation) were combined in a GIS environment. These factor-maps are: a) the Flow accumulation (F), b) the Land use (L), c) the Altitude (A), b) the Slope (S), e) the soil Erodibility (E), f) the Rainfall intensity (R), and g) the available water Capacity (C). The name to the proposed method is “FLASERC”. The flood hazard for each one of these factors is classified into five categories: Very low, low, moderate, high, and very high. The above factors are combined and processed using the appropriate ANN algorithm tool. For the ANN training process spatial distribution of historical flooded points in Greece within the five different flood hazard categories of the aforementioned seven factor-maps were combined. In this way, the overall flood hazard map for Greece was determined. The final results are verified using additional historical flood events that have occurred in Greece over the last 100 years. In addition, an overview of flood protection measures and adaptation policy approaches were proposed for agricultural and urban areas located at very high flood hazard areas. |
