A Knowledge-Based Multi-Criteria Decision Support System Encompassing Cascading Effects for Disaster Management

This paper describes a knowledge-based decision support system (KB-DSS) to improve the preparedness of crisis situations induced by natural and technological hazards. The proposed KB-DSS aims to manage the potential cascading effects generated by a triggering hazard assessing the possible event time histories based on interconnected probabilistic simulation models. From a methodological point of view, a decision model based on two Multi-Criteria Decision-Making (MCDM) algorithms follows a cascading effect simulation model. This combination allows to support the decision maker in comparing a set of mitigation strategies on the basis of their expected impacts and his priorities. The algorithm is based on an ensemble approach, which combines decisions over an array of possible impact scenarios, instead of only relying on the average impact scenario. An application of the KB-DSS to the case of a possible reactivation of Nea Kameni volcano in Santorini is presented to show how the proposed architecture could be applied to a real case. The proposed methodology supports the emergency planners in making the best decisions supporting them also in the choice of the best timing for the intervention.

[1]  Mohammad Bagher Menhaj,et al.  Fuzzy decision support system for crisis management with a new structure for decision making , 2010, Expert Syst. Appl..

[2]  G. Zuccaro,et al.  Economic impact of explosive volcanic eruptions: A simulation-based assessment model applied to Campania region volcanoes , 2013 .

[3]  Kuldar Taveter,et al.  Decision Making and Strategic Planning for Disaster Preparedness with a Multi-Criteria-Analysis Decision Support System , 2015, ISESS.

[4]  Jiangjiang Wang,et al.  Review on multi-criteria decision analysis aid in sustainable energy decision-making , 2009 .

[5]  Juscelino Almeida Dias,et al.  A stochastic method for robustness analysis in sorting problems , 2009, Eur. J. Oper. Res..

[6]  Matteo Gaeta,et al.  A knowledge-based framework for emergency DSS , 2011, Knowl. Based Syst..

[7]  Enrique Herrera-Viedma,et al.  A framework for context-aware heterogeneous group decision making in business processes , 2016, Knowl. Based Syst..

[8]  Antonio Lotito,et al.  Fast and Effective Decision Support for Crisis Management by the Analysis of People's Reactions Collected from Twitter , 2015, ADBIS.

[9]  M. E. J. Newman,et al.  Power laws, Pareto distributions and Zipf's law , 2005 .

[10]  Dirk Helbing,et al.  Disasters as Extreme Events and the Importance of Network Interactions for Disaster Response Management , 2006, Extreme Events in Nature and Society.

[11]  Mircea Boscoianu,et al.  Emerging Applications of the New Paradigm of Intelligent Decision Making Process: Hybrid Decision Support Systems for Virtual Enterprise (DSS-VE) , 2012 .

[12]  Chao Liu,et al.  Community-based collaborative information system for emergency management , 2014, Comput. Oper. Res..

[13]  D. Opitz,et al.  Popular Ensemble Methods: An Empirical Study , 1999, J. Artif. Intell. Res..

[14]  Yang Chen,et al.  Pairwise comparison matrix in multiple criteria decision making , 2016 .

[15]  Giulio Zuccaro,et al.  Time and space dependency in impact damage evaluation of a sub-Plinian eruption at Mount Vesuvius , 2013, Natural Hazards.

[16]  Daniela Fogli,et al.  Knowledge-centered design of decision support systems for emergency management , 2013, Decis. Support Syst..

[17]  Hitesh Nidhi Sharma,et al.  Disaster mitigation and preparedness using linked open data , 2013, J. Ambient Intell. Humaniz. Comput..

[18]  Szabolcs Harangi,et al.  Origin and ascent history of unusually crystal-rich alkaline basaltic magmas from the western Pannonian Basin , 2013, Bulletin of Volcanology.

[19]  Yi Peng,et al.  Evaluation of Classification Algorithms Using MCDM and Rank Correlation , 2012, Int. J. Inf. Technol. Decis. Mak..

[20]  Vincent Mousseau,et al.  Inferring an ELECTRE TRI Model from Assignment Examples , 1998, J. Glob. Optim..

[21]  Yi Peng,et al.  Evaluation of clustering algorithms for financial risk analysis using MCDM methods , 2014, Inf. Sci..

[22]  Jiuh-Biing Sheu,et al.  Dynamic Relief-Demand Management for Emergency Logistics Operations Under Large-Scale Disasters , 2010 .

[23]  W. Aspinall,et al.  Developing an Event Tree for probabilistic hazard and risk assessment at Vesuvius , 2008 .

[24]  F. Schultmann,et al.  A spatial-temporal vulnerability assessment to support the building of community resilience against power outage impacts , 2017 .

[25]  Kirsi Virrantaus,et al.  Shared situational awareness and information quality in disaster management , 2015 .

[26]  Andrew B. Whinston,et al.  FUTURE DIRECTIONS FOR DEVELOPING DECISION SUPPORT SYSTEMS , 1980 .

[27]  Demitris Paradissis,et al.  Evolution of Santorini Volcano dominated by episodic and rapid fluxes of melt from depth , 2012 .

[28]  Aurelio Tommasetti,et al.  Contextual Fuzzy-Based Decision Support System Through Opinion Analysis: A Case Study at University of the Salerno , 2016, Int. J. Inf. Technol. Decis. Mak..

[29]  Tamsin A. Mather,et al.  Distinguishing contributions to diffuse CO2 emissions in volcanic areas from magmatic degassing and thermal decarbonation using soil gas 222Rn–δ13C systematics: Application to Santorini volcano, Greece , 2013 .

[30]  R. Spence,et al.  Impact of explosive eruption scenarios at Vesuvius , 2008 .

[31]  Gang Kou,et al.  An integrated expert system for fast disaster assessment , 2014, Comput. Oper. Res..

[32]  Joel C. Gill,et al.  Reviewing and visualizing the interactions of natural hazards , 2014 .

[33]  Maria Laura Mastellone,et al.  Basic principles of multi-risk assessment: a case study in Italy , 2012, Natural Hazards.

[34]  Gianluca Pescaroli,et al.  Critical infrastructure, panarchies and the vulnerability paths of cascading disasters , 2016, Natural Hazards.

[35]  Dirk Helbing,et al.  Globally networked risks and how to respond , 2013, Nature.

[36]  D. Turcotte,et al.  The applicability of power-law frequency statistics to floods. , 2006 .

[37]  David Alexander,et al.  Scenario methodology for teaching principles of emergency management , 2000 .