AI News, AI to the rescue: 5 ways machine learning can assist during emergency situations

AI to the rescue: 5 ways machine learning can assist during emergency situations

At the wee hours of the night, on January 4 this year, over 9.8 million people experienced a magnitude 4.4 earthquake that rumbled across the San Francisco Bay Area.

In the past 6 months, the United States alone has witnessed four back-to-back storms from one brutal hurricane season, and a massive wildfire with almost 2 million acres of land ablaze.

Artificial intelligence and machine learning tools can also aggregate and crunch data from multiple resources such as crowd-sourced mapping materials or Google maps.

Machine learning approaches then combine all this data together, remove unreliable data, and identify informative sources to generate heat maps.

DigitalGlobe releases pre- and post-event imagery for select natural disasters each year, and their crowdsourcing platform, Tomnod, will prioritize micro-tasking to accelerate damage assessments.

They took pre and post-disaster imagery and utilized crowd-sourced data analysis and machine learning to identify locations affected by the quakes that had not yet been assessed or received aid.

This initiative is to help emergency call centers improve operations and public safety by using Watson’s speech-to-text and analytics programs.

Using Watson’s speech-to-text function, the context of each call is fed into the AI’s analytics program allowing improvements in how call centers respond to emergencies.

These vital stats can help on-the-ground aid workers to reach the point of crisis sooner and direct their efforts to the needy.

In addition, AI and predictive analytics software can analyze digital content from Twitter, Facebook, and Youtube to provide early warnings, ground-level location data, and real-time report verification.

In fact, AI could also be used to view the unstructured data and background of pictures and videos posted to social channels and compare them to find missing people.

The chatbot can interact with the victim, or other citizens in the vicinity via popular social media channels and ask them to upload information such as location, a photo, and some description.

AI for Digital Response (AIDR) is a free and open platform which uses machine intelligence to automatically filter and classify social media messages related to emergencies, disasters, and humanitarian crises.

AI systems and voice assistants can analyze massive amounts of calls, determine what type of incident occurred and verify the location.

Machine learning approaches such as predictive analytics can also analyze past events to identify and extract patterns and populations vulnerable to natural calamities.

A large number of supervised and unsupervised learning approaches are used to identify at-risk areas and improve predictions of future events.

Predictive machine learning models can also help officials distribute supplies to where people are going, rather than where they were by analyzing real-time behavior and movement of people.

Artificial neural networks take in information such as region, country, and natural disaster type to predict the potential monetary impact of natural disasters.

These advanced drones could expedite access to real-time information at disaster sites using video capturing capabilities and also deliver lightweight physical goods to hard to reach areas.

It has the potential to eliminate outages before they are detected and give disaster response leaders an informed, clearer picture of the disaster area, ultimately saving lives.

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