Predictive Analytics - Informing Disaster Preparation and Response

 A longtime presence in the NYC entrepreneurial community, Roger Coleman leads Disaster Tech and focuses on pathways of identifying dangers and preventing risks associated with natural disasters. One area of focus for Roger Coleman and his New York team is predictive analytics that help assess energy infrastructure risks and vulnerabilities.

This type of analysis relies on existing data sets in identifying patterns and quantifying the likelihood and potential severity of a disaster. Big data may be applied to a specific location, as when a municipality in a valley evaluates the risks of a major landslide, given weather, geology, vegetation, and other variables. In other cases such analytics can span vast areas of the globe, such as when assessing the risks of hurricane.

The value of big data is in the sheer amount of information that past natural disasters have provided. For example, every time a storm hits, sensors generate real time data on weather patterns, wind levels, and rainfall. Early warning signs that make it likely that Category 1 rainfall will develop into a Category 5 storm can be extrapolated from the thousands of storms on record.

At the same time, machine learning algorithms enable data to be automatically collected and analyzed, with each additional set of data points providing deeper and more accurate understanding. The end result is that local and national agencies are able to set up relief protocol appropriately sized to the actual level of risk and its local impact.

The Role of Large Technology Systems in Disaster Relief

 Based in New York City, Roger Coleman is a longtime entrepreneur who guides Disaster Tech and delivers solutions that enable municipalities, industries, and governments to mitigate the risk of natural disaster. One focus for Roger Coleman in NYC is deploying early warning systems that enable coordination of emergency response agencies and groups to earthquakes, hurricanes, and wildfires.

Over the past 15 years a number of large-scale systems have been developed that help reduce risks associated with often violent and unpredictable natural events. An example is the SERVAL project, which was launched following the devastating 2010 Haiti earthquake. It enables direct communication by cell phones in cases where network coverage is broken. In tandem with this, the TERA (Trilogy Emergency Relief Application) system of SMS texting was developed as a means of ensuring robust two-way communication by aid agencies and those impacted by natural disasters.

Following the 2015 Nepal earthquake, government agencies focused on another aspect of the equation: locating humans trapped deep underground when structures fall or cave in. The NASA Finder is able to detect a single human heartbeat, even when the victim is under as much as 30 feet of rubble or 20 feet of solid concrete.

Another aspect of mounting complex rescue operations in inhospitable terrain is having a comprehensive, accurate 3-D map of the area. The ALIRT (Airborne Ladar Imaging Research Testbed) system accomplishes just this, providing details such as road travel conditions and points where helicopters can land. Such information is vital in enabling aid agencies to plan out the delivery of essential items such as water, food, tents, medicines, and blankets.