Machine Learning can be a great ally in predicting environmental risk and protecting the environment and people. Thanks to accurate data, in fact, predictive models that learn over time are able to estimate the environmental risks in a specific area.
A catastrophic environmental event, in fact, modifies the territory and its entire ecosystem, from the fauna to the urban area, involving a change and adaptation of society as a whole.
Climate change data
In 2022, more than 400 natural disaster events were recorded, resulting in more than 31,000 deaths, around 40 million displaced persons and global economic losses exceeding USD 300 billion. The data speaks for itself, in the last 20 years 90% of disasters on Earth have been caused by extreme weather events, such as floods, storms and heat waves*. The challenges that mankind is already experiencing and those that it will have to face due to climate change are considerable and increasingly intense, which is why it is essential to protect people’s lives and try to anticipate the occurrence of dangerous environmental events as much as possible. Machine Learning can support environmental risk prediction activities.
Predictive models that learn over time
Machine Learning is a branch of Artificial Intelligence and can help scientists and engineers to study environmental events in depth and have an increasingly accurate edge in risk prediction. Thanks to the enormous amount of information that Machine Learning can process, in fact, collecting specific data about an area – e.g. weather events, vegetation present, human activity, and so on – makes it possible to construct data matrices that can produce an estimate of the occurrence of a specific event. By keeping the data constantly updated, the algorithms underlying Machine Learning are able to learn effectively and thus be increasingly representative of reality.
Sevara for environmental risk assessment
Omninext Group’s solution is Sevara: the Environmental Risk Assessment Service that uses the application of machine learning algorithms to estimate the risks present in a specific territory. Read more: https://sevara.io/en
*Sources: Report Aon | 2023 Weather, climate and catastrophe insight; Christian Aid | Counting the cost 2022; World Economic Forum | Global Risks Report 2020