Predictive data analytics uses powerful algorithm models to analyse past data to predict future trends. If applied correctly, predictive analytics can be used to improve disaster relief efforts, thus reducing the economic impact of natural calamities. For example, experts in the US are combining predictive analytics with satellite imagery to improve warning times for thunderstorms.
The importance of addressing natural disasters
Natural calamities are increasing in frequency and inflicting tremendous damage. In 2018, the United States experienced four storms back to back, which inflicted losses totalling $1 billion. However, it’s not just the USA suffering from calamities. Hurricane Maria swept through Puerto Rico, killing 3,000 people. Even now, the country is still dealing with setbacks dealt by the hurricane.
Scientists argue that the frequency of natural calamities is increasing, which means economies around the world will suffer billions of dollars in damage unless prevention systems are in place. Is there a way to reduce damage inflicted with better disaster management? Predictive data analytics could be the difference for many countries. Here are a few ways how.
Predictive data analytics reveals new rescue options
Mapping data is the key element for modern rescue operations. However, data needs to be interpreted effectively, otherwise, it is not useful to rescue workers. With predictive analytics, rescue workers can calculate the potential risks posed by a natural disaster and develop better disaster management plans.
Predictive analytics works by combining geographical data, real-time images, recently generated evidence and knowledge of what rescue operators have access to, to reveal dangers associated with a specific calamity. Hence, predictive analytics adopts a multilayered approach to data mapping, to deliver timely, accurate information to rescue workers.
A multilayered analysis is crucial for natural disasters because there are so many dangerous elements to consider. Neglecting one element can lead to disaster. For example, during the wildfires in the US, data analytics demonstrated how heat and smoke hindered fire workers from entering and exiting points of the burning land. Something that would have been difficult to accomplish without analytics.
Finding population-based ‘hotspots’
Time is crucial in disaster relief operations because rescue workers need to know where populations are based if they are to mount a timely rescue. A delay in the operation could have devastating effects on the civilian population, potentially leading to a humanitarian crisis. Hence, this is where predictive analytics is invaluable. Through geographic data, and other data sources, rescue operators can locate population concentrations. Therefore, they can find the biggest cluster of civilians closest to the natural disaster.
However, data analytics also reveals much more useful information about civilians. For example, data analytics can reveal where the elderly and handicapped, the people in most need of rescue, are living. By using this information, rescue workers will know where the most vulnerable groups are located. Therefore, they can make the most of their resources to mount timely rescue operations, by giving priority to those who need assistance the most.
Improved forecasting for on-ground activity
Civilians must move during a disaster, but where do they go to? Some cities or towns might have emergency protocols, but not everyone can follow them when disaster strikes. Is it possible for rescue workers to anticipate where people will go when disaster strikes and meet them halfway there? By using predictive data analytics, the answer to this question is, “yes”.
The data is predominantly procured from phone calls made to functioning towers. Local mobile operators can provide this data to analysts, who will track how and where people move during a natural disaster. The information will allow planners to develop more effective rescue operations and evacuation procedures for future natural calamities.
Better communication during a crisis
During a natural calamity, sharp, effective communication is crucial for getting people to evacuate safely. However, there are times when people don’t understand what they must do during an emergency, due to the nature of the message itself. However, by using data analytics, rescue planners can see what type of messages civilians understand best. This form of ‘message assessment’ works by cross-referencing phone-user behaviour with different messages, to see which messages help rescue operations, and which messages will complicate the rescue operation.
With natural disasters increasing in frequency, its imperative to develop tools that will improve rescue operations. While some natural disasters are unavoidable, it is possible to minimise the humanitarian and property damage, through predictive analytics. With data analytics, rescue operators can plan out new rescue operations, better emergency routes and communicate with more effectively civilians in a crisis. This, however, is just the tip of the iceberg because there are other ways to use data analytics to improve natural disaster management. For example, social media analytics can be used to analyse messages on Facebook, and Twitter when disaster strikes.
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