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Technical Papers
Jun 23, 2014

Bayesian Multiscale Modeling of Spatial Infrastructure Performance Predictions with an Application to Electric Power Outage Forecasting

Publication: Journal of Infrastructure Systems
Volume 21, Issue 2

Abstract

A number of models have been developed to estimate the spatial distribution of infrastructure impact during a natural hazard event. For example, statistical approaches have been developed to estimate the percentage of customers without power attributable to a hurricane, with the estimates made at a local geography level such as census tracts. Whereas some statistical infrastructure performance models use extensive covariate data that captures a significant amount of the spatial information, others use a limited number of covariates to enhance model simplicity and to reduce the cost and time associated with obtaining these data. However, in these simpler models, the omitted covariates result in loss of spatial information in the model, leading to a situation in which predictions from adjacent regions are more dissimilar than would be expected. In this paper, a tree-based statistical mass-balance multiscale model is developed to smooth the outage predictions at granular levels by allowing spatially similar areas to inform one another with the goals of: (1) reducing spatial error in simplified prediction models, and (2) yielding estimates at other levels of aggregation in addition to the native model resolution. A generalized density-based clustering algorithm is used to extract the hierarchical spatial structure. The noise regions (i.e., those regions located in sparse areas) are then aggregated using a distance-based clustering approach. The authors demonstrate this approach using outage predictions from Hurricane Ivan and develop outage prediction maps at different levels of granularity.

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Acknowledgments

This work is funded by grants from the National Science Foundation (NSF), grants 0968711 and 1149460. The support of the sponsor is gratefully acknowledged. Any opinions, findings, conclusions or recommendations presented in this paper are those of the authors and do not necessarily reflect the view of the National Science Foundation.

References

Achtert, E., Kriegel, H. P., and Zimek, A. (2013). “Example data sets for ELKI.” ELKI: Environment for developing KDD applications supported by index structures, 〈http://elki.dbs.ifi.lmu.de/wiki/DataSets〉 (Apr. 28, 2014).
Alon, U., et al. (1999). “Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays.” Proc. National Acad. Sci., 96(12), 6745–6750.
Ankerst, M., Breunig, M. M., Kriegel, H.-P., and Sander, J. (1999). “OPTICS: Ordering points to identify the clustering structure.” ACM SIGMOD Rec., 28(2), 49–60.
Broder, A. Z., Glassman, S. C., Manasse, M. S., and Zweig, G. (1997). “Syntactic clustering of the web.” Comput. Networks ISDN Syst., 29(8), 1157–1166.
Cook, T. D. (2005). “Emergent principles for the design, implementation, and analysis of cluster-based experiments in social science.” ANNALS Am. Acad. Political Soc. Sci., 599(1), 176–198.
Ester, M., Kriegel, H.-P., and Xu, X. (1996). “A density-based algorithm for discovering clusters in large spatial databases with noise.” Proc., Second Int. Conf. on Knowledge Discovery and Data Mining, AAAI, Palo Alto, CA, 226–231.
Fathian, M., Amiri, B., and Maroosi, A. (2007). “Application of honey-bee mating optimization algorithm on clustering.” Appl. Math. Comput., 190(2), 1502–1513.
Ferreira, M. A., and Lee, H. K. (2007). Multiscale modeling: A Bayesian perspective, Springer, New York, NY.
Frey, B. J., and Dueck, D. (2007). “Clustering by passing messages between data points.” Science, 315(5814), 972–976.
Guikema, S. D., Nateghi, R., and Quiring, S. (2013). “Predicting infrastructure loss of service from natural hazards with statistical models: Experiences and advances with hurricane power outage prediction.” European Safety and Reliability Conf. (ESREL), ESRA, Amsterdam, Netherlands.
Han, S.-R., Guikema, S. D., and Quiring, S. M. (2009a). “Improving the predictive accuracy of hurricane power outage forecasts using generalized additive models.” Risk Anal., 29(10), 1443–1453.
Han, S.-R., Guikema, S. D., Quiring, S. M., Lee, K.-H., Rosowsky, D., and Davidson, R. A. (2009b). “Estimating the spatial distribution of power outages during hurricanes in the Gulf coast region.” Reliab. Eng. Syst. Safety, 94(2), 199–210.
Hillhouse, J. J., and Adler, C. M. (1997). “Investigating stress effect patterns in hospital staff nurses: Results of a cluster analysis.” Soc. Sci. Med., 45(12), 1781–1788.
Jain, A. K. (2010). “Data clustering: 50 years beyond K-means.” Pattern Recognit. Lett., 31(8), 651–666.
Kolaczyk, E. D., and Huang, H. (2001). “Multiscale statistical models for hierarchical spatial aggregation.” Geograph. Anal., 33(2), 95–118.
Liu, H., Davidson, R. A., and Apanasovich, T. V. (2007). “Statistical forecasting of electric power restoration times in hurricanes and ice storms.” IEEE Trans. Power Syst., 22(4), 2270–2279.
Louie, M. M., and Kolaczyk, E. D. (2006). “A multiscale method for disease mapping in spatial epidemiology.” Stat. Med., 25(8), 1287–1306.
Menemenlis, D., Fieguth, P., Wunsch, C., and Willsky, A. (1997). “Adaptation of a fast optimal interpolation algorithm to the mapping of oceanographic data.” J. Geophys. Res., 102(C5), 10573–10584.
Moser, F., Ge, R., and Ester, M. (2007). “Joint cluster analysis of attribute and relationship data without a-priori specification of the number of clusters.” Proc., 13th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, AMC, New York, NY, 510–519.
Nateghi, R., Guikema, S. D., and Quiring, S. M. (2011). “Comparison and validation of statistical methods for predicting power outage durations in the event of hurricanes: Comparison and validation of statistical methods.” Risk Anal., 31(12), 1897–1906.
Nateghi, R., Guikema, S. D., and Quiring, S. M. (2013). “Power outage estimation for tropical cyclones: Improved accuracy with simpler models.” Risk Analysis,.
Sander, J., Qin, X., Lu, Z., Niu, N., and Kovarsky, A. (2003). “Automatic extraction of clusters from hierarchical clustering representations.” Advances in knowledge discovery and data mining, Springer, Berlin, Heidelberg, 75–87.
Wang, W., Yang, J., and Muntz, R. (1997). “STING: A statistical information grid approach to spatial data mining.” Proc., 23th Int. Conf. on Very Large Data Bases, Morgan Kaufmann Publishers, San Francisco, CA, 186–195.
Winkler, J., Dueñas-Osorio, L., Stein, R., and Subramanian, D. (2010). “Performance assessment of topologically diverse power systems subjected to hurricane events.” Reliab. Eng. Syst. Safety, 95(4), 323–336.

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Go to Journal of Infrastructure Systems
Journal of Infrastructure Systems
Volume 21Issue 2June 2015

History

Received: Oct 17, 2013
Accepted: Apr 18, 2014
Published online: Jun 23, 2014
Discussion open until: Nov 23, 2014
Published in print: Jun 1, 2015

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Allison Reilly, A.M.ASCE [email protected]
Postdoctoral Research Fellow, Dept. of Geography and Environmental Engineering, Johns Hopkins Univ., 3400 N Charles St., Ames Hall, Baltimore, MD 21218 (corresponding author). E-mail: [email protected]
Seth Guikema, A.M.ASCE
Assistant Professor, Dept. of Geography and Environmental Engineering, Johns Hopkins Univ., 3400 N Charles St., Ames Hall, Baltimore, MD 21218.

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