Disaster resilience and the future of technology

Advancement in technologies such as sensors, machine learning and Artificial Intelligence are helping communities better prepare for and respond to disasters. Companies and governments are working on applying new technology to old problems both because of the increasing severity of damage caused by natural disasters and the potential to reduce their enormous cost. The National Oceanic and Atmospheric Administration reports that climate and weather events caused $1.5 trillion in damage between 1980 and 2017. The technologies that companies and researchers are developing can be as simple as the Qatari organization AIDR using social media platforms to accurately map disaster sites and coordinating logistics or rely on new tools such as the Ontario utility company, Hydro One, using IBM’s outage-prediction tool to coordinate recovery.

The challenge of predicting earthquakes is a major example of how new technologies may be able to crack the challenge of predicting disasters. Past efforts to predict earthquakes by observing phenomena and basic machine learning in the 1980s and 1990s failed to be reliably accurate. Advances in machine learning, computer processing, and artificial intelligence could improve predictions by processing data at a scale that was not possible in the past. For example, researchers Paul Johnson and Chris Marone identified the acoustical sounds that shifting tectonic plates produce, which may help researchers understand earthquake timing in the future. Businesses and startups are also attempting to address the earthquake challenge. One Concern, a “startup company in California is using machine learning and artificial intelligence to advise fire departments about how to plan for earthquakes and respond to them,” by using information about building material, the natural environment, and live data to predict the consequences of an earthquake. Business and academic stakeholders have different advantages in this field; researchers are typically more transparent and open about their methods, while large businesses often have more capital to produce results that can be applied more quickly.

Although these tools have great potential to aid in disaster resilience, major roadblocks still exist in their development and implementation. First, because these new technologies are fairly new, the systems are not typically standardized or well integrated with those already in place, hindering effective communication. Second, because the technologies have not been tested, it can be challenging to convince companies or governments to invest in disaster prediction system with unclear financial returns. In spite of these barriers, the benefits of new technologies have enormous promise to help improve resilience against a variety of natural threats, epidemics, and other societal shocks.

Sources and Other Reading:

1. AI Helps Cities Predict Natural Disasters – Wall Street Journal
2. Betting On Artificial Intelligence To Guide Earthquake Response – NPR
3. Can Artificial Intelligence Predict Earthquakes? – Scientific American
4. Artificial Intelligence for Disaster Relief: A Primer – Lexalytics
5. Artificial Intelligence for Disaster Response – AIDR
6. Could Artificial Intelligence Help Us Predict the Next Epidemic? – Relief Web