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What is the impact of AI technologies in disaster risk reduction?
Artificial Intelligence (AI) and Machine Learning (ML) are transforming the way we work, and interact! No sector has been left untouched.
Disaster Risk Reduction (DRR) is one of the areas where the benefits and challenges of AI and ML in action have been demonstrated. Organisations have shared their successes, but also the key learnings that came along their journey of adopting and utilising AI.
In this white paper, you’ll find all you need to know, including:
Important definitions
The benefits of AI/ ML
Many different use cases
Why each is important
The type of AI/ML used
Best practices
Lessons learned
Threats and challenges of AI
And more!
Excited? We sure are!
Continue reading to learn more about what you can expect in the paper, or download your own copy now if you’ve already got a thirst for knowledge!
Why AI in Disaster Risk Reduction (DRR)?
AI technologies have emerged as transformative tools in the DRR space.
They are proving invaluable in various applications, such as predicting seasonal or extreme weather events, developing hazard maps, real-time disaster detection, and optimising resource allocation during emergencies.
But these uses are not without challenges. Issues such as system failures, cyber-attacks, and biases in AI models means that organisations have a lot to overcome.
On the bright side, this means that the adoption and implementation use cases of AI in the DRR space are amazing examples that we can learn from!
By understanding these use cases, organisations like yours can be in the best position to leverage the power of AI to build more resilient communities and mitigate the impact of disasters.
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Figure 1: Example applications of AI in DRR.
AI in DRR use cases
We hope you’re on the way to download the paper for yourself already! But if you haven’t yet, let’s dive into three of the use cases to give you a taste of what you can look forward to reading about…
Application of ML for the Preparation of Mass Movement Susceptibility Maps
By Alejandro Blandón-Santanao, Carlos Arturo García-Ocampo & Pedro León García-Reinoso
Focused on the Popayán-Mazamorras River road in Colombia, this paper presents a methodology supported by geographic information systems for the processing of variables associated with landslide susceptibility.
Using multivariate discriminant analysis, through the creation of an algorithm of machine learning systems, variables such as topography, geology, vegetation and infrastructure are integrated to build a map of susceptibility of mass movements along the road corridor.
One of the main learnings from this use case was that the validation of the product generated by the ML tool with the experience of social facilitators (road administrators) favors its acceptance, ensuring its use and improvement in the different road corridors of the country.
Zoning of vulnerability to fires based on Fuzzy Logic and Artificial Intelligence
By Procalculo Research and Development Group
The Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM) has developed a methodology for zoning the risk related to the occurrence of fires for a national analysis.
Fuzzy logic brings the computational decision process closer to human decision-making, making machines more capable of handling complex problems (Novák, 2012).
This methodology allows the study of natural phenomena at the local level so that municipal and departmental governments can take action to prevent, address, and manage the risk associated with said phenomena.
Proactive Disaster Risk Mitigation using year-ahead alerts with actionable analytics
By Subarna Bhattacharyya, CEO & Co-Founder Climformatics Inc., Fremont California
Lastly, this use case is an Early Alert System Decision Support Tool that provides year-ahead alerts with actionable analytics built using machine learning to make highly accurate climate predictions.
These tools span the interface between weather and climate, and can be as accurate for any given day many months ahead as the traditional nightly weather forecast for that day.
Several applied mathematical and statistical tools were used together with machine and deep learning tools like artificial neural networks.
One of the many lessons from this project was that there are many potential data sources, but data needs to be sourced with care.
Ready to learn more about AI and ML in DRR?
Artificial Intelligence and Machine Learning are transforming the Disaster Risk Reduction space (and beyond!) in ways we couldn’t have imagined a few years ago.
This blog only touched the surface of the information that can be available to you! To get your own copy of the white paper and find out all the details, you can download it for yourself here.
Let’s continue to work together, share our knowledge, and learn from each other to mitigate the impacts of disasters and save lives!
If you have any questions or want to talk more about this paper, shout us a holla to get in touch with us, we would love to hear from you 💚
Published on 20 March 2025, last updated on 20 March 2025
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