Applying AI and machine learning to tackling climate risk

Advances in the cutting-edge fields of artificial intelligence (AI) and machine learning (ML) are enabling organizations to discover and act on risks associated with climate change. This understanding is crucial for businesses, governments and other groups that want to incorporate climate resilience into their assets, strategies and supply chains. 

This article explains the basics of AI and ML and how they are fostering never-before-possible insights through a groundbreaking capability: Climate Intelligence (CI).

Since it was first coined in 1956, academics and technologists have wrestled over the exact definition of what constitutes artificial intelligence. All these years later, they still haven’t reached a consensus.  

Generally, AI refers to a machine's ability to adapt and apply what it already knows to new and unfamiliar scenarios. AI technology combines the perception of its surroundings with typically ‘human’ capabilities like reasoning, creativity, learning and planning to decide on the best set of actions to achieve a specific goal. AI technology is a broad umbrella term for a range of subfields, including machine learning.

The invention of the computer gave people a way to take in, store and process far more data than a human brain ever could. As technology advanced, the amount of data produced and collected increased exponentially. In 2020, the world generated an estimated 2.5 quintillion bytes of data every day - that’s 2.5 followed by 18 zeros. This incredible expansion of digital information created a new problem - big data. 

Big data solutions allow us to store, process and manage these vast amounts of data, but how do we extract useful information from this overload of data?

Machine Learning thrives under big data. ML is a type of AI technology with the capability to learn about the data, and identify patterns, much more efficiently in big data than humans can. Data Scientists apply ML to many different data sets to solve a wide variety of problems, from recommending songs to locating shipwrecks. It’s especially useful for tackling big datasets - such as climate data.

We use algorithms. Algorithms are a set of instructions that tell a computer what to do with data. Almost every piece of technology uses algorithms to transform data into useful information about the world. For instance, your smartphone’s operating system will contain an algorithm that will turn the flash on when your phone detects low light levels.  

ML algorithms do more than describe a set of problems to answer, they tell computers how to learn about the data. This is a method of data analysis based on the idea that systems can learn from data, identify patterns and make decisions. 

We can think of ML algorithms as providing the machine with an approach to solving a set of problems. Data scientists train the algorithm with previous datasets so it can identify patterns in the data and apply them to an action. As we add more data, the ML algorithm continually learns to produce better predictions.

Fraud prevention is a good example of ML algorithms in action. By feeding them datasets containing the characteristics of fraudulent operations, developers can train ML algorithms to separate legitimation action from fraud. The result is a security system that can tackle financial fraught far faster than human monitors.

Machine learning has proven extremely useful in interpreting climate data to understand climate-related risks. Scientists have generated a huge amount of information about the workings of our climate system - far too much for any one company or person to condense into a single cohesive model. 

In addition, the climate system is fundamentally interconnected. For instance, higher global temperatures increase the chances of extreme heat. Extreme heat leads to droughts, which can raise the risk of forest fires. Forest fires release carbon into the atmosphere, contributing to global warming. To understand the impacts of climate change at the asset level - where many of its effects manifest - we need a picture of the entire system. Machine Learning is the way to discover these relations in the vast amounts of climate data.

The application of ML to climate science has made granular, asset-level analysis of climate-related risks possible through a new capability called Climate Intelligence (CI). Climate Intelligence is asset-level intelligence for managing climate risk. By leveraging ML algorithms to unite fragmented climate data, and the complex interplay between climate change and human activity, CI enables everyone to discover and analyze physical risk quickly, scalably and in a way that is inclusive of multiple hazards and their interactions with one another (multivariate). 

The clarity provided by CI enables companies to get visibility on their climate risk and make assets, supply chains and disruption networks climate-resilient, by investing in adaptive measures to protect against impacts like extreme storms and rising sea levels. In a recent market perspective, analyst firm IDC called CI “a strategic priority” for organizations worldwide, and a primary solution to the $23 trillion problem climate change could pose to the global economy by 2050.

Cervest’s Climate Intelligence is powered by Earth Science AI™. This is the advanced science and technology behind our CI products including EarthScan™ and EarthCap™. Fusing remote sensing, peer-reviewed science, data modeling and pioneering machine learning technology, Cervest’s CI provides everyone with dynamic climate risk analysis on individual built assets, or across portfolios. 

To find out more about how Cervest’s CI can de-risk your decision making and build resilience into your decision-making, sign up to the EarthScan Starter Program.