It can help decision makers answer questions such as “When?” and “How bad?”
—and “How many people are in harm’s way?”
Hurricane Dorian wreaked havoc in the Bahamas. Massive fires raged through the
Amazon forest. A 7.1-magnitude earthquake and aftershocks rocked Southern California
this summer. Kerala, India, suffered the biggest flood in nearly a century.
It is painfully obvious that natural disasters all over the world are inflicting
increasing amounts of damage—and it is likely that even more destructive events
will occur in the future. But how can we defend and protect ourselves against the
inevitable disasters to come?
The answer lies in our ability to better forecast, plan for and respond to natural
disasters. New technologies that can analyze massive amounts of data are very promising
tools that can help community leaders and emergency managers make more informed
decisions. These technologies, developed from the field of machine learning, can
supplement and enhance existing disaster response programs.
Machine-learning technologies can help decision makers more accurately answer urgent
questions such as: When will the disaster hit? How destructive will it be? What areas
will be hit hardest, and how many people live and work in these areas? What buildings
will be most vulnerable? Will there be power outages, and if so, where? What equipment
and resources will be needed and for how long? How much will the disaster response
effort cost? And so forth.
In a nutshell, machine learning lets computers mimic human learning to analyze large
amounts of data from past disasters to generate new insights about current and future
similar events. The computer is trained to "think," processing information and developing
insights that far exceed what a human brain can compute.
There's a wealth of data available on past disasters that machine learning can leverage.
In fact, it's already being used to improve disaster response. For example, some utilities
are using machine-learning tools developed in my research group, in collaboration with
Steven Quiring of Ohio State University, to predict power outages from hurricanes and other
severe weather events. The utilities report that machine learning has provided critical
information to help them improve decision making.
In another example, a startup has developed a cross-hazard platform using both engineering
-based and machine-learning models to provide information to community leaders and emergency
managers that enhance both long-term disaster resilience and short-term disaster response.
Another nonprofit startup is using data analytics and mapping to connect disaster victims
to first responders and volunteer groups.
Moreover, machine-learning technologies do have limitations. They can only process and
analyze information that has been input into the computer. For example, if data for an
extremely large disaster is not part of the data set, machine-learning technologies likely
cannot make accurate predictions for a comparable event in the future. Machine-learning
predictions come with uncertainty that can be difficult for decision makers to fully
It is important to emphasize that machine learning in no way replaces human decision making;
it only supplements expert judgement and traditional disaster response methods. This is a
critical difference from how machine learning is used in other areas, such as self-driving
vehicles, where the technology seeks to at least partially replace human decision making.
Machine learning cannot and should not replace traditional methods of disaster response.
Expert human judgement is absolutely critical, given the complexity and magnitude of the
I know many people are skeptical about machine learning. They worry that the science is
unproven and there isn't enough data to predict future events. But these are just myths.
Machine learning, when used correctly and based on solid data relevant to future situations,
has been proven in many industries. Enormous amounts of data do exist that can be leveraged
for different events and situations, even in the realm of natural disasters.
As floods, earthquakes and wildfires inflict increasing amounts of damage in the future,
machine learning should be an essential part of disaster response programs. If we don't use
it, we are depriving community leaders and emergency managers of an important tool that
can improve their decision making in critical times.
Why Machine Learning Is Critical for Disaster Response, Seth Guikema， https://blogs.scientificamerican.com/observations/why-machine-learning-is-critical-for-disaster-response/