Predicting Natural Disasters With Your Smartphone

Predicting Natural Disasters With Your Smartphone

by | May 28, 2021 | Tech

As of April 2021, there were an estimated 3.8 billion smartphone users in the world, or nearly half the global population of 7.8 billion. That has implications not only for communication and entertainment, but also safety. 

That has been shown during the coronavirus pandemic, as nations like Germany and Ireland have used contact tracing apps to great effect. (That is not true in the U.S., however. Such apps have largely gone unused, in no small part because of privacy concerns.)

Still, smartphone apps have shown potential to keep users safe in other ways, one being in their ability to predict natural disasters. Such events as earthquakes, tsunamis, hurricanes, floods, wildfires, heatwaves, and droughts kill an average of 60,000 people a year. Scientists, as a result, have been extremely interested in developing mechanisms that not only prevent these disasters but also keep humankind at arm’s length from potential negative effects.

Smartphones come with integrated and highly precise sensor technology designed to assess environmental conditions like humidity, temperature, and even ground vibrations. For meteorologists to make sense of such data, they need a huge volume of this information. 

Thanks to the Internet of Things (IoT) and blockchain, engineers can collect a treasure trove of data; scientists can consequently use this data to make accurate predictions on prospected weather patterns. This technology can prove crucial in helping predict the potential occurrence of heavy rains and flash floods within an area, for example. 

The good thing with such data is that it is accurate and available at a moment’s notice. Smartphones come equipped with GPS technology which can help such meteorologists to determine the particular area affected accurately.

An additional convenience offered by smartphones is their ability to disseminate information as accurately, precisely, and timely as possible. In case, for instance, meteorologists accurately predict the occurrence of natural disasters within an area, they can immediately disseminate cautionary information to the residents, warning them of impending floods, heatwaves, fires, or even hurricanes. Such information is crucial in helping to reduce and manage the adverse effects of natural disasters.

The benefits that smartphones provide in the entire weather industry are further enhanced by the fact that these devices can remotely transmit data through satellites. Thanks to blockchain technology, the data can be instantly processed and even reliably used to make predictions through artificial intelligence and machine learning. The observed weather patterns can be compared to previous occurrences to predict impending disasters accurately.

For instance, if the data shows characteristic patterns in temperatures, atmospheric tides, and atmospheric pressure, then this information can be used to predict the occurrence of hurricanes. The consistent relaying of smartphone data thereafter can be used to determine the prospected path of such a developing hurricane, thereby offering advance cautionary information to the public.

How Machine Learning is Influencing Wearable Technology

How Machine Learning is Influencing Wearable Technology

by | Oct 31, 2016 | Sports, Tech

Thanks to the Internet of Things, we’re awash in more data than we know what to do with. Data from our cars, our watches, our toothbrushes — collecting information is the easy part, but handling it can be complicated. Machine learning can make sense of data, and the implications could help IoT — and wearables specifically — really take off.

When machines are designed to recognize patterns and update their algorithms accordingly, they can make wearable technology more useful for consumers. An article on Warable.com explains some of the opportunities machine learning already presents for technology companies and their wearable products.

Take Google as one example. Android Wear’s “Google Now” app is getting a new feature called “Now On Tap” that uses machine learning to provide contextual suggestions without any prompting. Between search, email, text, location, calendar, and apps, the amount of data that can be mined is huge, and the software can learn a lot about you and what you need at any given moment. It might suggest movie times and trailers when a friend texts you about seeing a film, or quick lunch spots based on your physical location and schedule.

Apple is working on similar technology through which the Apple Watch will recommend more and more relevant apps based on user data. Machine learning could also empower Siri to make informed, proactive suggestions.

Since fitness is the most popular form of wearable, machine learning is influencing the efficacy of health software as well. The Microsoft Band will use the company’s Intelligence Engine to learn how your daily activities influences your exercise routine. The system could, for example, determine whether a high amount of meetings correlates with a slower run, or less sleep.

On the healthcare end, wearables with sensors can inform medical professionals of patient data such as air quality, humidity, steps, times opening the fridge, or using the bathroom. This provides a more comprehensive picture of a patient’s wellness.

Wearables can also track and learn about wearer’s emotions, body movements, fertility, and medication compliance.

All of this together may seem amazing and terrifying at the same time. I see it as an inevitability of technology; we have the data, so it follows that we’ll program the technology to take advantage of it. As long as it’s being used with consent of the user to the user’s advantage, it has the possibility to benefit all of mankind.