There is nothing artificial about climate change. The weather patterns we see around the planet, from colossal floods to catastrophic fires to devastating drought, there is no mistaking the fact that we need to use all the resources in our technological toolbox to fight natural or human-caused disasters. One such advancement is Artificial Intelligence (AI). Even though it sometimes gets a bad rap, especially from those pesky Alexa stories, it is becoming genuinely helpful in myriad ways, including the somewhat unforseen ability to predict natural disasters. Such an technological ability not only saves time and money—it saves lives.
The Way It Works
In Australia alone, the total economic cost of natural disasters is growing and will reach $39 billion per year by 2050. These costs include significant, and often long-term, social impacts, including death and injury, as well as impacts on employment, education, community networks, health, and wellbeing. More than nine million Australians have been impacted by a natural disaster or extreme weather event in the past 30 years. And the number of people affected annually is expected to grow as the intensity—and in some areas, frequency—of these events continues to grow.
In the last few years, AI has become more and more prevalent and powerful. It can diagnose diseases, select music, give directions, create shopping lists—you name it. If this is possible, could AI understand, predict and manage natural disasters, such as floods, fires, and earthquakes, better than humans beings? Recent preliminary research says yes—AI could help us monitor such natural and unpredictable disasters and could perhaps even deliver more accurate early warning messages, and soon.
Early testing leveraging social media indicators, crowdsourcing, and Natural Language Programming (NLP) can be used to create a log of reported flood data. First, the volume of flood-related tweets is found linearly correlated to precipitation, and Tweet data points are found clustered around geos with the most activity. Then crowdsourcing flood photos are processed by computer vision algorithms and recommendations are given to improve the big data on flood monitoring. In this way, we can successfully train AI to monitor for flooding much the same way that humans would manually, but at scale.
Shaking Up Disaster Prediction
Companies are now using machine learning and artificial intelligence to advise fire departments about how to plan for earthquakes and respond to them. California-based company, One Concern, uses its algorithms to take a lot of the guesswork out of the planning process for disaster response by making accurate predictions about earthquake damage. It’s one of a handful of companies rolling out artificial intelligence and machine learning systems that could help predict and respond to floods, cyber attacks, and other large-scale disasters.
The truth is, AI is only as good as its data collection practices and quality of data input. In the case of earthquake prevention, it takes a combination of a survey of the natural topography and buildings surrounding the area. Then the computer looks at the live, real-time data, such as the magnitude of the quake, the traffic in the area of the quake and the weather at the time of the event. The computer then uses this information to make predictions about what would happen if an earthquake occurred in a particular area—and then implements it further by analyzing past earthquakes to see whether their predictions are any good, revising the predictive models accordingly.
Speedier Data Gathering, Modeling and Problem Solving
In the past, scientists and software engineers would painstakingly gather data to model events, such as earthquakes—and it could sometimes take years. Now, the new models appear in a matter of minutes. This type of AI-powered predictive software can save first responders a lot of time and taxpayer money. Globally, governments, emergency response services, and insurers are constantly looking for ways to make communities more resilient to natural disasters. But we must do more.
In June of 2018, IBM announced a partnership with the U.N. Human Rights Office, Red Cross, and Linux Foundation, launching “Call for Code,” a global open source initiative to build technology up so the world can better prepare for natural disasters. The initiative brings together 22 million developers from across the globe to apply technologies like AI, IoT, and blockchain to help minimize the impact of disasters.
Another such academic mandate involves universities who are creating or beefing up their programs and curriculum for emergency preparedness. For example, The University at Albany in New York has developed its own sort of emergency response to the infrastructure, environmental, and national security problems in the age of terror threats and climate change. The College of Emergency Management, Homeland Security, and Cybersecurity (CEHC) is the first of its kind in the nation.
The CEHC curriculum includes a focus on cybersecurity, data analytics, the and information sciences. At the college, students learn how to handle emergencies, including prevention, immediate response and dealing with the aftermath.
Intelligence Is Relative
AI is a mimicking algorithm of human judgements—nd in this way, it is better than humans in terms of speed and volume, but not in terms of quality. We still have a long way to go in terms of training AI-enabled systems to think critically or creatively in real-time, like humans are apt to do. Translation—humans are still superior in this regard, at least for now. It takes a diversity of minds to help solve some of the world’s most pressing challenges. There will be no shortage of natural disasters in the future, so we must find better ways of mitigating the risks by working together with our robot cousins.
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