Machine Learning (MI) can save us from Disasters

• What is Machine Learning..?

Let us first understand what is Machine Learning before we move on to how it can benefit cybersecurity. Machine learning is a type of AI (Artificial Intelligence), which gathers large chunks of data and trains machines to make devices act smarter and as intelligent as humans. Here are ways how machine learning can help organizations for cybersecurity.

by Naveen Joshi – Founder and CEO of Allerin, Mumbai
Works in Big Data, IoT , AI and Blockchain.

Machine learning (MI) in disaster management holds the potential to use predictive analytics for alerting about upcoming calamities. Apart from predictions, it can also assist rescue teams by informing about damage levels.

Natural disasters are inevitable when a natural disaster like a cyclone, storm, floods, or tsunami occurs; humans cannot do much to avoid the wrath of a calamity. However, people realize that the severity of a natural disaster can minimize with the assistance of adequate disaster management techniques. Numerous natural disasters took place in 2017, out of which the monsoon floods in Bangladesh holds the highest number of deaths by killing more than 1200 people. Organizations are leveraging numerous technologies to combat natural disasters; one of them is machine learning. Machine learning is a technology that learns from its users about how they do particular tasks. When they learn from their users, they understand how to do it without the assistance of a human being. Governments realized that ML can transform the future of disaster management. Machine learning in disaster management can be a technology that can assist authorities to perform better under situations of natural disaters.

Machine learning in disaster management to predict disasters

Predictive analytics is one of the features of machine learning that allows organizations to know about the forthcoming events. Machine learning does the work of predictive analytics by tracking patterns and analyzing information to know about the possibilities of the same occurring in future.

Predictive analytics can assist officials by providing them details on the specific areas where they are less affected. This information helps with respect to sending rescue teams to particular regions and supports authorities by empowering them with information on areas minimally affected by the calamity.
Apart from notifying disaster management teams about areas, predictive analytics can also supply insights for the authorities to know the impact of a particular calamity affected area. There are numerous organizations, such as Optima Predict, that allow authorities to scan through areas affected by tragedies.

Machine learning in disaster management to rescue survivors

When a calamity strikes, one of the toughest tasks for the rescue authorities is to scan through areas affected by the disaster. When authorities want to recover people from a disaster-affected zone, the risks of officials losing their lives to save civilians is high.

By using machine learning, authorities obtain actionable insights about people stuck in a particular area and how they can be rescued by mapping through the zone using image mapping techniques. By using these techniques, authorities get a heat map of areas that require immediate response and supplies.

Machine learning has applications across numerous industries; disaster management is an area that can benefit tremendously from the assistance of machine learning. Government authorities should now look for ways through which they can use this technology. Training employees and authorities to work with this technology also stands as a viable option for the government to enhance their disaster management abilities.

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