Artificial intelligence for disaster risk reduction

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Introduction

We are living in an era marked by disasters, which coincides with the tremendous speed of technological growth. The abundance of available data and sensor technology poses an additional challenge: the sheer volume of data exceeds our ability to extract value from it.

In this setting, artificial intelligence emerges as a powerful frontier technology that has the potential to transform the speed and method of data collection and analysis. By altering data processing processes, it enables quick and informed decision-making, such as through accurate real-time data analysis.

Discover artificial intelligence and machine learning applications ranging from early warning systems to evacuation models, as well as other intriguing disaster risk reduction innovations.

Themes
Disaster risk management

Knowledge base

A cyclist rides past a destroyed car dealership in the aftermath of the 1994 Northridge earthquake
Research briefs

Building safer cities with AI: Machine learning model enhances urban resilience against liquefaction

The model provides contour maps that show the depth of soil bearing layers, helping city planners identify areas vulnerable to liquefaction.
Shibaura Institute of Technology (SIT)
Woman with an umbrella walking through a flooded street in city of Salvador, Brazil in 2015
Research briefs

UMD researcher trains AI to predict diarrheal outbreaks related to climate change

New study uses AI models to create early warning system for disease outbreaks resulting from climate change-related extreme weather.
University of Maryland
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Documents and publications

Improving earthquake prediction accuracy in Los Angeles with machine learning

This research breaks new ground in earthquake prediction for Los Angeles, California, by leveraging advanced machine learning and neural network models. We meticulously constructed a comprehensive feature matrix to maximize predictive accuracy.
Update

AI is helping seismologists find the next monster earthquake

Zachary Ross uses machine learning to detect imperceptible earthquakes in California, revealing hidden faults. His pioneering work enhances our ability to face future seismic events by improving earthquake preparedness.
National Geographic Society
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Documents and publications

Hydro-informer: a deep learning model for accurate water level and flood predictions

This study aims to develop an advanced deep learning model, Hydro-Informer, for accurate water level and flood predictions, emphasizing extreme event forecasting.
Antigua and Barbuda Hurricane Damage
Update

Eyeing the damage of hurricane season

As hurricane season continues to affect communities, researchers at Texas A&M are using AI to assess the damage.
Texas A&M University System
officer checking the condition of the solar panel, early warning system, in anticipation of landslides
Research briefs

Machine learning could improve extreme weather warnings

A deep learning technique could reduce the error in 10-day weather forecasts by more than 90%, allowing communities to better prepare for extreme events such as heat waves.
Eos - AGU
Flooded street
Research briefs

AI-trained CCTV in rivers can spot blockages and reduce floods

Machine learning-equipped camera systems can be an effective and low-cost flood defence tool, researchers show.
University of Bath
Update

AI to the rescue: enhance disaster early warnings with tech tools

AI is enhancing disaster early warnings by improving accuracy, speed, and accessibility, but global standards are needed to ensure trust and fairness. Collaboration between sectors is crucial for developing effective disaster management solutions.
Nature Publishing Group
Update

Where surfaces are impervious

Many areas in North Rhine-Westphalia are paved over by impervious surfaces such as roads, housing estates and industrial sites. While this is obvious from aerial photographs, it is difficult to analyze.
Ruhr University Bochum | Ruhr-Universität Bochum

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