According to a report by the Coalition for Disaster Resilient Infrastructure (CDRI), the global annual average loss in key infrastructure sectors due to climate-related disasters was estimated to be between $301 billion and $330 billion.

As climate change accelerates, the resilience of our roads, bridges, and power grids is increasingly under threat.

But artificial intelligence (AI) is stepping in as a powerful tool in building climate-resilient infrastructure. Through real-time data analysis, predictive modeling, and smart maintenance strategies, AI is transforming how we anticipate and adapt to a changing climate.

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Why Infrastructure Fails Under Climate Stress

Climate extremes, such as intense rainfall, heatwaves, hurricanes, and rising sea levels, push infrastructure beyond its design limits. Much of our global infrastructure was built under the assumption of stable, predictable weather—an assumption that no longer holds.

When infrastructure fails, the consequences are vast: economic losses, human casualties, disruptions in essential services, and setbacks in climate adaptation efforts. This is where AI comes into play.

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How AI Predicts Extreme Weather Impacts

AI algorithms are uniquely capable of detecting patterns in vast, complex datasets—such as satellite imagery, historical weather data, IoT sensors, and climate models. 

One of AI’s most valuable applications is in predictive maintenance—spotting signs of wear or damage before they lead to costly failures.

• IoT-enabled infrastructure: Sensors embedded in bridges, pipelines, and buildings continuously feed data to AI systems that monitor structural integrity.
  
• Anomaly detection: AI can flag subtle changes—like vibrations or pressure shifts—that signal future problems.‍
• Optimized maintenance schedules: AI doesn’t just detect issues, it can also recommend the most efficient time to repair or reinforce infrastructure, reducing downtime and saving costs.

These predictive models have helped us build:

• Early Warning Systems with AI: AI can forecast extreme weather with greater accuracy, providing critical lead time for emergency response and risk mitigation.
• Flood and Landslide Risk Prediction: Machine learning models trained on topographic, rainfall, and soil data can pinpoint areas at high risk of floods or landslides.
• Climate Risk Mapping and Resilient Design: AI helps cities map vulnerabilities—like heat islands or aging infrastructure—so they can prioritize climate-resilient upgrades.

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Case Studies: AI In Action

The power of artificial intelligence (AI) to process huge amounts of data and help humans make decisions is transforming industries.

Here are 5 ways researchers are using AI to build sustainable and resilient infrastructure:

1. Monitoring How Icebergs are Melting:

AI has been trained to measure changes in icebergs 10,000 times faster than a human could do it.

This will help scientists understand how much meltwater icebergs release into the ocean – a process accelerating as climate change warms the atmosphere.

Scientists at the University of Leeds in the United Kingdom say their AI can map large Antarctic icebergs in satellite images in just one-hundredth of a second, reports the European Space Agency.

These measurements are crucial for infrastructure planning, especially in coastal areas and regions reliant on glacial meltwater.

These AI-backed predictions on sea level rise are informing decisions about coastal protection measures, infrastructure relocation, and adaptation strategies. Understanding iceberg melt rates also aids in assessing the impact on freshwater resources and potential flooding risks.

2. Google’s AI-Powered Flood Forecasting in India and Bangladesh:

Google's Flood Forecasting Initiative employs AI models to provide real-time flood forecasts and alerts. Initially launched in India's Patna region in 2018, the service expanded to cover all of India and parts of Bangladesh by 2020, aiming to help protect over 200 million people in India and 40 million in Bangladesh.

By 2023, across Africa, Google had launched this initiative to 80 countries across the Asia-Pacific region, Europe, and South and Central America, covering some 460 million people globally.

Google estimates that AI helped them provide more accurate information on riverine floods up to 7 days in advance.

3. New York City’s Subway System:

‍New York City's subway system uses a combination of technologies, including AI, to prevent and mitigate flood damage.

Researchers have utilized digital twin technology to simulate flood scenarios and assess the structural resilience of the New York City subway system under varying flood intensities. By integrating real-time environmental and structural data, they analyzed water ingress rates and identified high-risk segments within the subway.

4. Smart Road Monitoring in the European Union:

The city of Verona, Italy, is testing smart sensor technology to ease traffic congestion and improve road safety. The system collects and transmits traffic data to an operations center supported by local servers, aiming to enhance urban mobility through real-time monitoring.

5. Using Data and AI to Improve Barcelona’s Water Supply:

The city of Barcelona has a pioneering approach to water supply analysis by using big data, machine learning, and artificial intelligence.

Aigües de Barcelona, which looks after water supplies in the city, is collaborating with the Barcelona Supercomputing Center (BSC) on ways to improve supplies and help plants work better. 

In one project, going live this year, the BSC is using big data to enhance the workings of a water treatment plant in Sant Joan Despí, near Barcelona. The plant uses reverse osmosis to remove salt from water taken from the Llobregat River.

As part of the process, pumps push water through a membrane to filter out the salt. When the membranes get dirty it takes more power to pump the water through. Frequent cleaning is costly and cuts the amount of water that can be processed. 

The project aims to find a happy medium by looking at nine years’ worth of data from dozens of sensors in the Sant Joan Despí plant. This should help keep the plant running more smoothly while cutting its costs and carbon footprint. 

“The idea is to be able to predict the state of the membranes so the cleaning can be planned in advance,” says Fernando Cucchietti, head of the data analytics and visualization group at the BSC.

The BSC is also working with Aigües de Barcelona on how the company’s control center handles the management of events in the water network. The project involves building a model of Barcelona’s water system, so managers can make decisions more easily.

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Toward Climate-Resilient Cities and Communities

Despite these advancements, AI won’t prevent climate change on its own. But it can make our cities smarter, safer, and more responsive. By pairing AI technologies with forward-looking policy, sustainable design, and community engagement, we can build infrastructure that’s ready for the climate shocks of tomorrow.

As we race to adapt to a warming world, AI stands out not just as a tool for efficiency, but as a pillar of resilience. Governments, engineers, and planners must embrace AI-driven solutions now—not just to repair what’s broken, but to design a future that withstands the climate crises to come.