How predictive analytics tools help us detect natural disasters

ural disasters. The direct results of our less-than-thoughtful actions and the effect they have on the environment. 

Annually, natural disasters cause more than 60,000 deaths, on average, and destroy billions worth of infrastructure and property.

In the pre-modern world, these events caused millions of deaths annually. Floods and droughts were the worst offenders in terms of the number of lives lost.

Fortunately, we don’t see as many natural disasters like the eruption of Mount Krakatoa, which killed 36,000 people in 1883, or the 1931 China floods, which killed a staggering four million people (according to the highest estimate).

Advancements in technology and architecture accompanied by predictive data analytics have reduced some of the worst effects of natural disasters. Communities around the world now live with less fear of the environmental threats we face. 

Recent events like the 2021 floods and the 2020 wildfires in Australia, however, have brought the focus back on large-scale natural disasters. 

In this post, we dive deeper into how predictive data analytics can help us detect natural disasters in advance and prepare authorities and civilians alike to handle what’s ahead. 

Predictive analytics tools use live data to detect natural disasters 

Storms, earthquakes, and floods are the three biggest natural disasters we contend with in the modern world.

In recent years, the frequency and severity of storms and floods have increased while the number of lives lost has declined compared to previous decades.

While there is debate over what has caused this increase in disasters—climate change, unfortunately, is still a divisive issue—the usefulness of disaster detection systems has been a major reason for the drop in the number of lives lost.

Scientists have now learned to leverage the power of data analytics to detect disasters before they affect our lives, giving authorities enough time to plan how to manage these disasters.

Japan, for example, is famous for its earthquake warning system, which helps the country ensure the safety of millions of residents. This is highly useful given that the country is located in one of the most seismically active regions of the world.

Japanese scientists have designed a system that uses predictive analytics tools to analyse data from earthquake hotspots and alert residents in vulnerable areas, helping them take precautionary measures in the events of tsunamis or earthquakes.

Other countries, including Australia, now have robust systems to detect floods, which are often caused by rainstorms. Meteorological departments around the world monitor and gather data continuously from satellite images, weather forecasts, and sensors, which are then used to predict weather patterns and help authorities take necessary precautions.

Australia, for example, has now issued flood warnings to more than 40% of its population as predictive analytics tools have detected that the weather could worsen over the coming days, which will, without a doubt, help residents protect themselves better against the threats of climate change.

We’re saving lives and managing natural disaster better with predictive data analytics 

Data analytics is used in many industries around the world to great effect, including the detection and management of natural disasters.

Natural disasters, when handled ineffectively, can cause unimaginable damage to both human lives and our infrastructure. Fortunately, scientists around the world are leveraging the power of data to build robust and accurate disaster detection systems to help us be more prepared for what we can’t prevent. 

These systems are helping governmental authorities protect their constituents against the worst of climate change. 

One certainty among the many uncertainties of the future is that these predictive analytics tools will save millions of lives for years to come.