Statistical tools for populating/predicting input data of risk analysis models

G. L. Pita, R. Francis, Z. Liu, J. Mitrani-Reiser, S. Guikema, J. P. Pinelli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

By quantifying economic risk due to damage to building stock, regional loss models for natural hazards are critical in the creation of regional policies, including evacuation strategies and zoning. The increasingly complex interaction between natural hazards and human activities requires more and more accurate data to describe the regional exposure to potential loss from physical damage to buildings and infrastructure. While databases contain information on the distribution and features of the building stock, infrastructure, transportation, etc., it is not unusual that portions of the information are missing from the available databases. Missing or low quality data compromise the validity of regional loss projections. Consequently, this paper uses Bayesian Belief Networks and Classification and Regression Trees to populate the missing information inside a database based on the structure of the available data. A case study is presented to evaluate results.

Original languageEnglish (US)
Title of host publicationVulnerability, Uncertainty, and Risk
Subtitle of host publicationAnalysis, Modeling, and Management - Proceedings of the ICVRAM 2011 and ISUMA 2011 Conferences
Pages468-476
Number of pages9
DOIs
StatePublished - Jun 13 2011
EventInternational Conference on Vulnerability and Risk Analysis and Management, ICVRAM 2011 and the International Symposium on Uncertainty Modeling and Analysis, ISUMA 2011 - Hyattsville, MD, United States
Duration: Apr 11 2011Apr 13 2011

Publication series

NameVulnerability, Uncertainty, and Risk: Analysis, Modeling, and Management - Proceedings of the ICVRAM 2011 and ISUMA 2011 Conferences

Conference

ConferenceInternational Conference on Vulnerability and Risk Analysis and Management, ICVRAM 2011 and the International Symposium on Uncertainty Modeling and Analysis, ISUMA 2011
CountryUnited States
CityHyattsville, MD
Period4/11/114/13/11

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ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality

Cite this

Pita, G. L., Francis, R., Liu, Z., Mitrani-Reiser, J., Guikema, S., & Pinelli, J. P. (2011). Statistical tools for populating/predicting input data of risk analysis models. In Vulnerability, Uncertainty, and Risk: Analysis, Modeling, and Management - Proceedings of the ICVRAM 2011 and ISUMA 2011 Conferences (pp. 468-476). (Vulnerability, Uncertainty, and Risk: Analysis, Modeling, and Management - Proceedings of the ICVRAM 2011 and ISUMA 2011 Conferences). https://doi.org/10.1061/41170(400)57