AI, the new frontier in climate change risk assessment


Global warming is exacerbating weather and climate extreme events and the interaction between different forms of hazards triggered by climate change will cause future cross-sectoral impact affecting a variety of natural and human systems. 
Research can improve the understanding of these interactions and dynamics, in order to support decision makers in managing current and future climate change risks, thanks to an improved ability to predict expected risks and quantify their impact. 
The scientific community in recent years has started testing new methodological approaches, technologies and tools, among which is the application of machine learning, which can help exploit the potential of large amounts and variety of environmental monitoring data available today (big data). 
In a study ‘Exploring machine learning potential for climate change risk assessment’, a team of scientists from the CMCC Foundation and Ca’ Foscari University of Venice conducted an in-depth review of more than 1,200 articles on the subject, published in the last 20 years, highlighting the potential and limitations of machine learning in this field. 
CMCC Foundation is an Italian research centre named as Euro-Mediterranean Centre on Climate Change. 
“Machine learning is a branch of artificial intelligence,” explains a researcher at the CMCC Foundation and Ca’ Foscari University, Venice, and the main author of the study, Federica Zennaro. 
“By simulating the processes of the human brain, certain mathematical algorithms can understand the relationships between a set of input data in order to predict the required output. In our research, we identified that floods and landslides are the most analyzed events through machine learning models, probably because they are the most relevant and frequent around the world.” 
Moreover, the study reveals that machine learning has two major potentials that make it particularly interesting when applied to this field of study, a global release from the CMCC Foundation said. 
The first is that the said algorithms can learn from data: the more data, the better algorithms learn. Thanks to its ability to analyze and process large amounts of data, machine learning allows researchers to disentangle complex relationships underlying the functioning of socio-ecological systems, exploiting the big data collected from various sources, including sensors for environmental analysis at high temporal frequency, social media, satellite data and images, and drones. 
The second is that they can combine different types of data, thus enabling an assessment of the risk extent while considering all its dimensions. These include not only the triggering hazard (for example, an increase in rainfall) but also the vulnerability and exposure of the socio-economic system at stake, which are crucial factors in an evaluation of overall impact. 
“For example, consider a model that is trained with detailed data on flood events over the past 20 years, including their location and information on the affected context (urban or natural). This model can project, in a scenario characterized by future climate conditions, what the probability of an event happening at a certain point will be and calculate its risk of causing harmful impact to society and the environment,” Zennaro said.