# Strong Ground Motion Sensor Network for Civil Protection Rapid Decision Support Systems
###### Abstract  
Strong motion sensor networks deployed in metropolitan areas are able to provide valuable information for civil protection Decision Support Systems (DSSs) aiming to mitigate seismic risk and earthquake social-economic impact. To this direction, such a network is installed and real-time operated in Chania (Crete Island, Greece), city located in the vicinity of the seismically active south front of the Hellenic Subduction Zone. A blend of both traditional and advanced analysis techniques and interpretation methods of strong ground motion data are presented, studying indicative cases of Chania shaking due to earthquakes in the last couple years. The orientation independent spectral acceleration as well as the spatial distribution of the strong ground motion parameters such as the Peak Ground Acceleration (PGA), Peak Ground Velocity (PGV), Peak Ground Displacement (PGD) and Arias Intensity observed at the urban area of Chania are presented with the use of a Geographic Information System (GIS) environment. The results point to the importance of the strong ground motion networks as they can provide valuable information on earthquake hazards prior to and after detrimental seismic events to feed rapid systems supporting civil protection decisions for prevention and emergency response. strong ground motion; sensor network; decision support system; spectral acceleration;arias intensity; PGA; PGV; PGD; GIS 2021 21 April  
erties, structures, and networks due to isolated or combined hazards, constrained in space and time. In this context, earthquake hazard mitigation and seismic risk reduction to metropolitan areas is a complex, multidisciplinary, spatiotemporally dynamic problem, difficult to formulate. Thus, it requires extensive and continuous studies to understand the physical mechanisms as well as the catastrophic implications of earthquakes. As populations increase and economies grow in big or mega urban areas, an augmented seismic risk is expected, posing accessed pressure to civil protection decision-makers to abate this risk. Thus, the development and use of Decision Support Systems (DSSs) for Natural Hazard Risk Reduction (NHRR) gains significant importance [2]. A contemporary DSS is a mixture of high-end computer systems and data warehouses supporting efficient management of large volumes of data and complex information. Its fundamental scope is to improve and simplify decision processes and to stimulate the effectiveness of decisions to be made in all the hierarchical levels of decision-makers, belonging to diverse operators (organizations, agencies, authorities), end-users, and stakeholders [1]. DSSs for NHRR are classified as model-oriented DSSs that adopt model integration from a palette of simulation models for risk analysis (hazard, exposure, vulnerability modelling), optimization models for test and assess risk-reduction options, as well as suggestion models which provide suggested decisions for risk-reduction measures and emergency response, eliminating human bias. Nowadays, DSSs for NHRR take advantage of web-technologies, artificial intelligence, data mining, real-time, parallel processing, Geographic Information System (GIS), remote sensing, mobile and satellite communications. It is prevalent that proactive seismic risk reduction is preferable and much more effective, in all aspects, compared to civil protection actions for response and recovery. Inescapable outcomes should be the public dissemination of information on seismic risk and eventually seismic early warning. Hence, seismic-risk reduction planning and prevention strategies should be short-, intermediate- and long-term monitoring and assessing risk variations [3; 4; 5; 6]. To meet the ultimate objectives, seismic risk criteria are determined and examined. [PERSON] et al.