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How to assess impact of weather related costs using analytics


This article was contributed by Vaibhav Waghmare

The 2014 New Year deep freeze may have cost US economy an estimated $5 billion. This is total aggregated economic impact due to the Polar Vortex splitting and running down south of the North pole. This estimate includes costs incurred from 

  • Cancellation of flights
  • Power failures
  • Insurance claims
  • Damages caused by burst water pipe and frozen sewer lines
  • Increased cost of heating and electricity bills
  • Loss of industrial production due to temporary closures
  • Overtime budgets and repair cost
  • Road maintenance costs

Similar to Hurricane Sandy, one can argue that the impact of these natural disasters have been reduced due to improved forecasting abilities. Weather driven calamities can be better managed with the help of new Satellite Systems, Weather information Systems, hurricane detection systems technologies. All these systems directly operate on real time weather and associated data. Enormous amount of data is generated and processed by these systems to provide predictions ahead of time. Even though we don’t have the power to stop weather related events, we can certainly reduce the effects of these disasters on human life and National resources.

We conducted a quick research on various natural disasters to explore the trend of deaths and injuries due to these disasters, and what we found was interesting. The first plot which shows that the total number of deaths has been on the rise since 1996 (watching the orange trend line) is misleading. To understand the benefits of early warnings and improved forecasts, one has to pay attention to the second chart (green trend line) which shows that the number of deaths per event has been continuously decreasing. This clearly attests to the fact that predictive analytics (of the type employed by weather folks) is helping!

descriptive analytics to show impact of weather disasters resized 600

You can explore this interactive dashboard to understand how various natural and weather related disasters impacted different states in the US over the last 18 years.

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Climatology or long term forecasting will use models built on data. The question is: to what level are current short term productive models using historic data vs. physics. If it is low, then is there an opportunity to predict even better by incorporating more history?
Posted @ Saturday, January 25, 2014 7:14 AM by Sital Shah
Good question. I dont think physics based models today are better than empirical models based on historic data.
Posted @ Saturday, January 25, 2014 8:21 AM by Bala Deshpande
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