View Item 

Show simple item record

Artificial Intelligence for Natural Hazards Risk Analysis: Potential, Challenges, and Research Needs

dc.contributor.authorGuikema, Seth
dc.date.accessioned2020-07-02T20:32:20Z
dc.date.availableWITHHELD_12_MONTHS
dc.date.available2020-07-02T20:32:20Z
dc.date.issued2020-06
dc.identifier.citationGuikema, Seth (2020). "Artificial Intelligence for Natural Hazards Risk Analysis: Potential, Challenges, and Research Needs." Risk Analysis 40(6): 1117-1123.
dc.identifier.issn0272-4332
dc.identifier.issn1539-6924
dc.identifier.urihttps://hdl.handle.net/2027.42/155885
dc.description.abstractArtificial intelligence (AI) methods have seen increasingly widespread use in everything from consumer products and driverless cars to fraud detection and weather forecasting. The use of AI has transformed many of these application domains. There are ongoing efforts at leveraging AI for disaster risk analysis. This article takes a critical look at the use of AI for disaster risk analysis. What is the potential? How is the use of AI in this field different from its use in nondisaster fields? What challenges need to be overcome for this potential to be realized? And, what are the potential pitfalls of an AI‐based approach for disaster risk analysis that we as a society must be cautious of?
dc.publisherWiley Periodicals, Inc.
dc.subject.otherartificial intelligence
dc.subject.othernatural hazards
dc.subject.otherpredictive modeling
dc.titleArtificial Intelligence for Natural Hazards Risk Analysis: Potential, Challenges, and Research Needs
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelBusiness (General)
dc.subject.hlbtoplevelBusiness and Economics
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/155885/1/risa13476_am.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/155885/2/risa13476.pdf
dc.identifier.doi10.1111/risa.13476
dc.identifier.sourceRisk Analysis
dc.identifier.citedreferenceRivero‐Calle, S., Gnanadesikan, A., Del Castillo, C. E., Balch, W. M., & Guikema, S. D. ( 2015 ). Multidecadal increase in North Atlantic coccolithophores and the potential role of rising CO2. Science, 350 ( 6267 ), 1533 – 1537.
dc.identifier.citedreferenceHuang, Z., Yu, Y., Gu, J., & Liu, H. ( 2016 ). An efficient method for traffic sign recognition based on extreme learning machine. IEEE Transactions on Cybernetics, 47 ( 4 ), 920 – 933.
dc.identifier.citedreferenceKabir, E., Guikema, S. D., & Quiring, S. M. ( 2019 ). Predicting thunderstorm‐induced power outages to support utility restoration. IEEE Transactions on Power Systems, 34 ( 6 ), 4370 – 4381.
dc.identifier.citedreferenceKaplan, S., & Garrick, B. J. ( 1981 ). On the quantitative definition of risk. Risk analysis, 1 ( 1 ), 11 – 27.
dc.identifier.citedreferenceKircher, C. A., Whitman, R. V., & Holmes, W. T. ( 2006 ). HAZUS earthquake loss estimation methods. Natural Hazards Review, 7 ( 2 ), 45 – 59.
dc.identifier.citedreferenceLaRocca, S., Johansson, J., Hassel, H., & Guikema, S. ( 2015 ). Topological performance measures as surrogates for physical flow models for risk and vulnerability analysis for electric power systems. Risk Analysis, 35 ( 4 ), 608 – 623.
dc.identifier.citedreferenceLiong, S. Y., & Sivapragasam, C. ( 2002 ). Flood stage forecasting with support vector machines 1. JAWRA Journal of the American Water Resources Association, 38 ( 1 ), 173 – 186.
dc.identifier.citedreferenceMaes, S., Tuyls, K., Vanschoenwinkel, B., & Manderick, B. ( 2002 ). January. Credit card fraud detection using Bayesian and neural networks. In Proceedings of the 1st international Naiso Congress on Neuro fuzzy technologies (pp. 261–270).
dc.identifier.citedreferenceMosavi, A., Ozturk, P., & Chau, K. W. ( 2018 ). Flood prediction using machine learning models: Literature review. Water, 10 ( 11 ), 1536.
dc.identifier.citedreferencePomerleau, D., & Jochem, T. ( 1996 ). Rapidly adapting machine vision for automated vehicle steering. IEEE Expert, 11 ( 2 ), 19 – 27.
dc.identifier.citedreferenceQuiring, S. M., Schumacher, A. B., & Guikema, S. D. ( 2014 ). Incorporating hurricane forecast uncertainty into a decision‐support application for power outage modeling. Bulletin of the American Meteorological Society, 95 ( 1 ), 47 – 58.
dc.identifier.citedreferenceSchneider, P. J., & Schauer, B. A. ( 2006 ). HAZUS: Its development and its future. Natural Hazards Review, 7 ( 2 ), 40 – 44.
dc.identifier.citedreferenceShashaani, S., Guikema, S. D., Zhai, C., Pino, J. V., & Quiring, S. M. ( 2018 ). Multi‐stage prediction for zero‐inflated hurricane induced power outages. IEEE Access, 6, 62432 – 62449.
dc.identifier.citedreferenceSRA ( 2018 ). Society for risk analysis glossary, updated August 2018. Retrieved from https://sra.org/sites/default/files/pdf/SRA%20Glossary%20-%20FINAL.pdf
dc.identifier.citedreferenceSuryanita, R., & Adnan, A. ( 2012 ). Intelligent monitoring system on prediction of building damage index using neural‐network. Telkomnika, 10 ( 1 ), 155.
dc.identifier.citedreferenceTripathi, S., Srinivas, V. V., & Nanjundiah, R. S. ( 2006 ). Downscaling of precipitation for climate change scenarios: A support vector machine approach. Journal of Hydrology, 330 ( 3‐4 ), 621 – 640.
dc.identifier.citedreferenceWang, S., Hong, L., Ouyang, M., Zhang, J., & Chen, X. ( 2013 ). Vulnerability analysis of interdependent infrastructure systems under edge attack strategies. Safety science, 51 ( 1 ), 328 – 337.
dc.identifier.citedreferenceWinkler, J., Duenas‐Osorio, L., Stein, R., & Subramanian, D. ( 2010 ). Performance assessment of topologically diverse power systems subjected to hurricane events. Reliability Engineering & System Safety, 95 ( 4 ), 323 – 336.
dc.identifier.citedreferenceAven, T. ( 2013 ). A conceptual framework for linking risk and the elements of the data–information–knowledge–wisdom (DIKW) hierarchy. Reliability Engineering & System Safety, 111, 30 – 36.
dc.identifier.citedreferenceAven, T. ( 2017 ). Improving risk characterisations in practical situations by highlighting knowledge aspects, with applications to risk matrices. Reliability Engineering & System Safety, 167, 42 – 48.
dc.identifier.citedreferenceBadr, H. S., Zaitchik, B. F., & Guikema, S. D. ( 2014 ). Application of statistical models to the prediction of seasonal rainfall anomalies over the Sahel. Journal of Applied Meteorology and Climatology, 53 ( 3 ), 614 – 636.
dc.identifier.citedreferenceBaroud, H., & Barker, K. ( 2018 ). A Bayesian kernel approach to modeling resilience‐based network component importance. Reliability Engineering & System Safety, 170, 10 – 19.
dc.identifier.citedreferenceBurton, S., Gauerhof, L., & Heinzemann, C. ( 2017 ). September. Making the case for safety of machine learning in highly automated driving. In International Conference on Computer Safety, Reliability, and Security (pp. 5–16). Springer, Cham.
dc.identifier.citedreferenceChan, P. K., & Stolfo, S. J. ( 1998 ). August. Toward scalable learning with non‐uniform class and cost distributions: A case study in credit card fraud detection. In KDD (Vol. 1998, pp. 164–168).
dc.identifier.citedreferenceDueñas‐Osorio, L., & Vemuru, S. M. ( 2009 ). Cascading failures in complex infrastructure systems. Structural Safety, 31 ( 2 ), 157 – 167.
dc.identifier.citedreferenceFrancis, R. A., Guikema, S. D., & Henneman, L. ( 2014 ). Bayesian belief networks for predicting drinking water distribution system pipe breaks. Reliability Engineering & System Safety, 130, 1 – 11.
dc.identifier.citedreferenceGuikema, S. D. ( 2009 ). Natural disaster risk analysis for critical infrastructure systems: An approach based on statistical learning theory. Reliability Engineering & System Safety, 94 ( 4 ), 855 – 860.
dc.identifier.citedreferenceGuikema, S. D., Nateghi, R., Quiring, S. M., Staid, A., Reilly, A. C., & Gao, M. ( 2014 ). Predicting hurricane power outages to support storm response planning. IEEE Access, 2, 1364 – 1373.
dc.identifier.citedreferenceHan, S. R., Guikema, S. D., Quiring, S. M., Lee, K. H., Rosowsky, D., & Davidson, R. A. ( 2009 ). Estimating the spatial distribution of power outages during hurricanes in the Gulf coast region. Reliability Engineering & System Safety, 94 ( 2 ), 199 – 210.
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Name:
risa13476_am.pdf
Size:
530.5KB
Format:
PDF
View/Open
Name:
risa13476.pdf
Size:
74.47KB
Format:
PDF
View/Open

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.