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Interaction between environmental andtechnological systems: toward an unifying approach for Risk Prediction
 

Interaction between environmental and technological systems: toward an unifying approach for Risk Prediction

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    Interaction between environmental andtechnological systems: toward an unifying approach for Risk Prediction Interaction between environmental and technological systems: toward an unifying approach for Risk Prediction Presentation Transcript

    • Interaction between environmental and technological systems: toward an unifying approach for Risk PredictionVittorio Rosato, Antonio Di Pietro, Giuseppe Aprea, Roberta Delfanti, Luigi La Porta, Josè R. Marti, Paul Lusina and Maurizio Pollino
    • Conceptual framework• Main goal (long-term plan): design and development of a new class of Decision Support Systems (DSS) integrating, in an unique framework, systems able to provide an efficient and accurate risk assessment based on events prediction and their impacts.• Methodological approach: modeling and simulation techniques of environmental systems dynamics in DSS, aiming at forecasting crisis scenario. Event prediction is also associated to impacts predictions (damaged citizens and lands, loss of services for Critical Infrastructures, CIs).• With respect to the emergency response issues related to critical events (e.g. natural disasters or nuclear accidents), the recent advances in geo- informatics, communication and sensor technologies have been opening new opportunities.• An interactive DSS based on GIS approach could support the public government to address activities to emergency management and damage evaluations. 2
    • Event prediction: Extreme weather events• When a specific perturbation strikes CIs several outages might result. They can also be worsened by cascading effects triggered by systemss interdependency, which propagate faults and outages from one technological system to another.• An accurate prediction of the weather conditions and of their effects can help in estimating the probability that a specific (catastrophic) event might take place.• A similar approach can be pursued also in case of non-predictable natural events, such as earthquakes. WEATHER FORECAST, WEATHER FORECAST, Meteorological data ++ Meteorological data ESTIMATION OF ESTIMATION OF 11 MONITORING MONITORING Local Area Model Local Area Model FLOODING FLOODING NETWORK DATA NETWORK DATA PERTURBATIONS Simulations PERTURBATIONS Simulations EXTERNAL EXTERNAL WEATHER FORECAST, WEATHER FORECAST, Historical climate and Historical climate and ESTIMATION OF ESTIMATION OF 22 MONITORING MONITORING Geophysical data ++ climate Geophysical data climate LANDSLIDE LANDSLIDE NETWORK DATA NETWORK DATA models simulations models simulations MONITORING MONITORING Geophysical data ++ ESTIMATION OF ESTIMATION OF Geophysical data 33 NETWORK DATA NETWORK DATA Time series analysis EARTHQUAKE EARTHQUAKE Time series analysis 3
    • Risk prediction of CIs• After the prediction phase, the system simulates the functioning of the involved infrastructure(s) to evaluate the associated service reduction.• This (wide area) prediction can be then provided to the crisis manager, with some advance (24-48 hours); this should allow to take some measure (if possible) to mitigate the impact of the event and to estimate possible repercussion on other infrastructures. 11 22 33 EXTERNAL EXTERNAL INFRASTRUCTURE INFRASTRUCTURE CI1 1 CI DATA + + DOMAIN PERTURBATIONS PERTURBATIONS DATA DOMAIN SIMULATION SIMULATION 44 INFRASTRUCTURE ESTIMATION OF ESTIMATION OF INFRASTRUCTURE CRISIS CRISIS CI2 2 CI DATA + + DOMAIN DATA DOMAIN ACCIDENTS OR ACCIDENTS OR SIMULATION SCENARIO SCENARIO SIMULATION ATTACKS ATTACKS SYSTEM OF SYSTEM OFSYSTEMS (SoS) SYSTEMS (SoS) INTERDEPENDENCY INTERDEPENDENCY INFRASTRUCTURE INFRASTRUCTURE DYNAMIC RISK ASSESSMENT MODEL MODEL CIK K CI INFRASTRUCTURE INFRASTRUCTURE DATA + + DOMAIN DATA DOMAIN SIMULATION SIMULATION IMPACT IMPACT EVALUATION EVALUATION CIN N CI INFRASTRUCTURE INFRASTRUCTURE DATA + + DOMAIN DATA DOMAIN SIMULATION SIMULATION Downgrading to Crisis 4
    • I2Sim (Infrastructure InterdependenciesSimulator)• I2Sim - Interdependency Infrastructure Simulator: discrete event simulator.• Simulate the Emergent Behaviour of a System of interconnected infrastructures.• In Emergency times it is useful in understanding the impacts of decisions.• Core components: Tokens, channels, production cells, distributors, storage cells. 5
    • Decision Evaluation using I2Sim b c b c Policy a a d d ranking using I2Sim a c decision support b d simulator d Analyse each candidate policy Policy with the best predicted outcome is implemented, e.g. best hospital operation 6
    • A case study: Sendai Tsunami andEarthquake• Sendai Tsunami and Earthquake case study modeled with I2Sim Light Injury Hospital (LIH), SeriousInjury Hospital (SIH) or shelter zone. Three impact zones represent geographic districts where An ambulance network performs injured people are located the evacuation Treated patients 7
    • Nuclear waste release from plants• An on-going research work is focused on the investigation and development of an innovative methodology for mapping nuclear waste release from plant after a severe accident, assessing the impact on Italian territory and evaluating the consequences for people and environment, by using geospatial methods.• This GIS-DSS is conceived as a comprehensive tool that integrates model predictions and geospatial data for mapping radionuclides diffusion, scenario testing and disaster planning.• The results become tools for an interactive DSS, supporting the public stakeholders to quickly evaluate consequences for people and environment and to address - in the post-event phase - the emergency management. 8
    • Nuclear waste release from plants Simulation Map: Simulation Map: 137 137Cs diffusion in atmosphere Cs diffusion in atmosphere (Values in Bq/m33 )) (Values in Bq/m 36 hours after the event 36 hours after the event 9
    • Nuclear waste release from plants GIS Base Layers GIS Base Layers ••Google layers Google layers ••Urban Areas Urban Areas ••Land Use/Land Cover Land Use/Land Cover from CORINE 2006 from CORINE 2006 ••NPP NPP The WebGIS interface allows The WebGIS interface allows to visualize and analyse the to visualize and analyse the Simulation Maps geo-spatial data and geo-spatial data and Simulation Maps (Radionuclides dif- thematic maps stored in the thematic maps stored in the (Radionuclides dif- fusion in atmosphere fusion in atmosphere system by means of basic system by means of basic and ground deposition) and ground deposition) functionalities such as: functionalities such as: description and description and characterization of the area characterization of the area of interest, production of of interest, production of thematic maps (e.g., thematic maps (e.g., radionuclides deposition), radionuclides deposition), Scenarios impact scenarios (e.g. Scenarios impact scenarios (e.g. impact on population, map impact on population, map Time evolution of deposition in agricultural of deposition in agricultural Time evolution area, etc.) and their time area, etc.) and their time evolution. evolution. 10
    • Nuclear waste release from plants:example of scenario Example of Scenario: Example of Scenario: ••137Cs Cumulated ground Cs Cumulated ground 137 deposition (Bq/m2)) over Urban deposition (Bq/m2 over Urban Areas. Areas. •• Detailed reports. Detailed reports. 11
    • Risk assessment of infrastructures uponearthquakesPredicting and mapping seismic vulnerability, assessing potential impacts ofdisastrous events.GIS-DSS architecture:1)Geospatial database system;2)Local GIS application foranalysing and modelling theseismic event and its impactsand supporting post-eventemergency management;3)WebGIS module for sharingthe geo-information amongthe decision makers involved indisaster impact assessmentand response management. 12
    • Risk assessment of infrastructures uponearthquakes• The main aims of the GIS-DSS is to make geographic data, thematic maps and probable damage Scenarios available to specific end-users and, potentially, to the public. Buildings Vulnerability Map Example of expected damage scenario. The map is categorized considering six different levels of damage as described below (level representation shown on the left)• GIS-DSS supports a real-time monitoring of the vulnerability of structures and infrastructures within the area of interest, providing a preliminary assessment of the expected damages on structures and infrastructures a few seconds after the main shock. 13
    • Conclusions • We have presented a number of implementations of kernel of DSS in different areas of risk analysis. • The unifying picture is the awareness that the inclusion, in the DSS workflow, of a capability of predicting environmental threats and that of considering the environment as a propagator of perturbations is a key ingredient for the effectiveness of these systems. • This approach to risk analysis is intrinsically multidisciplinar, as it involves the clustering of a number of expertises, from those related to CIs to those of geomatics, weather forecasting, oceanography, seismology etc. • This approach will certainly foster in the future a new generation of risk assessment/management tools which will enable an easier and more effective management of crises. 15
    • Authors information Vittorio Rosato1,4,*, Antonio Di Pietro1, Giuseppe Aprea1, Roberta Delfanti2, Luigi La Porta1, Josè R. Marti3, Paul Lusina3 and Maurizio Pollino1 ITALIAN NATIONAL AGENCY FOR NEW TECHNOLOGIES, ENERGY AND (1) SUSTAINABLE ECONOMIC DEVELOPMENT (ENEA) - TECHNICAL UNIT FOR ENERGETIC AND ENVIRONMENTAL MODELLING, CASACCIA RESEARCH CENTRE, ROMA (ITALY) ITALIAN NATIONAL AGENCY FOR NEW TECHNOLOGIES, ENERGY AND (2) SUSTAINABLE ECONOMIC DEVELOPMENT (ENEA) - TECHNICAL UNIT MARINE ENVIRONMENT AND SUSTAINABLE DEVELOPMENT, S.TERESA (ITALY) (3) DEPT. OF ELECTRICAL AND COMPUTER ENGINEERING, UNIVERSITY OF BRITISH COLUMBIA, VANCOUVER B.C. (CANADA) (4) YLICHRON S.R.L., ROMA (ITALY) *Corresponding author, vittorio.rosato@enea.it WWW home page: http://www.enea.it 16