EGU22-1230
https://doi.org/10.5194/egusphere-egu22-1230
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

IBM Operations Risk Insights with Watson:  a multi-hazard risk, AI for Natural Disaster Management use case

Thomas Ward1 and Rinku Kanwar2
Thomas Ward and Rinku Kanwar
  • 1IBM, Chief Data Office, United States of America (tomward@us.ibm.com)
  • 2IBM, Chief Information Office, United States of America (v2rinku@us.ibm.com)

Overview:

Operations Risk Insight (ORI) with Watson is an IBM AI application on the cloud.  ORI analyzes thousands of news sources and alert services daily.  There are too many data sources, warnings, watches and advisories for an individual to understand.  For example, during a week in 2021 with record wildfires, hurricanes and COVID hotspots across the US, thousands of impacting risk events hit key points of interest to IBM globally and were analyzed in real time.  

Which events impacted IBM’s business, and which didn’t? ORI has saved IBM millions of dollars annually for the past 5 years.  Our non-profit disaster relief partners have used ORI to respond more effectively to the needs of the vulnerable groups impacted by disasters.  Find out how disaster response leaders identify severe risks using Watson, the Hybrid Cloud, Big Data, Machine Learning and AI.

Presentation Objectives:

The objectives of this session are:

  • Educate the audience on a pragmatic and relevant IBM internal use case for an AI on the Cloud application, using many Watson and The Weather Company API's, plus machine learning running on IBM's cloud.
  • Obtain feedback and suggestions from the audience on how to expand and improve the machine learning and data analysis for this application to expanded the value for natural disaster response leaders. .
  • Inspire others to create their own grass roots cognitive project and learn more about AI and cloud technologies.
  • Discuss how this relates to the Call for Code and is used by Disaster Relief Agencies for free to assist the most vulnerable in society.

References Links:  

  • ORI has been featured in two Cloud Pak for Data (CP4D) workbooks:  CP4D Watson Studio Tutorial on Risk Analysis: https://dataplatform.cloud.ibm.com/analytics/notebooks/v2/f2ee8dbf-e6af-4b00-90ca-8f7fee77c377/view and the Flood Risk Project: https://dataplatform.dev.cloud.ibm.com/exchange/public/entry/view/def444923c771f3f20285820dc072eac  Each demonstrate the application and methods for Machine Learning to be applied to AI for Natural Disaster Management (NDM). 
  • IBM use case for non-profit partners: https://newsroom.ibm.com/ORI-nonprofits-disaster
  • NC Tech article: https://www.ednc.org/nonprofits-and-artificial-intelligence-join-forces-for-covid-19-relief/
  • Supply Chain Management Review (SCMR) interview: https://www.scmr.com/article/nextgen_supply_chain_interview_tom_ward
  • Supply Chain navigator article: http://scnavigator.avnet.com/article/january-2017/the-missing-link/

How to cite: Ward, T. and Kanwar, R.: IBM Operations Risk Insights with Watson:  a multi-hazard risk, AI for Natural Disaster Management use case, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1230, https://doi.org/10.5194/egusphere-egu22-1230, 2022.

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