The increasing frequency and intensity of extreme weather events, driven by climate change, pose urgent global threats. Artificial Intelligence (AI) and Machine Learning (ML) are vital tools in addressing this crisis, enhancing prediction, mitigation, and adaptation capabilities. These technologies improve forecasting accuracy, accelerate disaster response, optimise resource allocation, and contribute to broader sustainability goals.

However, challenges exist, including AI’s reliance on historical data (limiting prediction of unprecedented events), data quality and bias issues. 

To fully leverage AI/ML, strategic recommendations will include:

  • developing hybrid AI-physics models, 
  • investing in robust global data collection, 
  • fostering collaboration, 
  • ensuring responsible, equitable AI deployment.

Why is AI Essential for Climate Resilience?

The global environment faces an alarming rise in extreme weather events. These events range from floods and droughts to wildfires and heatwaves, and they cause significant economic losses, displacement, and fatalities. Climate change is the primary driver, making advanced prediction and mitigation critical.

AI and ML are emerging as transformative tools across climate science and sustainability efforts. They offer advanced capabilities for detecting, forecasting, and assessing climate risks. They do this by processing vast quantities of real-time and historical data to identify complex patterns.

AI-driven models are often faster and more reliable than traditional methods. They use less computing power and cost, which democratizes access to advanced forecasting for developing nations.

AI for Prediction

Hurricane and Cyclone Forecasting: AI models like Google DeepMind’s WeatherNext Gen can generate ensemble forecasts (up to 50 scenarios) for cyclones. It can predict formation, paths, intensity, and structure up to 15 days in advance. GraphCast can also accurately predicts hurricane paths.

Wildfire Prediction: AI can analyse past wildfires, real-time weather, lightning predictions, and vegetation to predict wildfire likelihood and spread. It integrates with cameras for early detection of nascent fires. LSU’s DeepFire project uses a cooperative system of prediction, detection, and spread modeling to pinpoint high-risk areas and deploy resources preemptively.

Heatwave Prediction: Machine learning techniques, including Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN), are effective in predicting extreme heat events.

AI for Mitigation

AI and ML models help mitigate extreme weather impacts by informing strategic responses and enhancing resilience:

Resource Management and Disaster Response: AI can optimize resource allocation by processing real-time data from sensors, drones, satellite images, and social media. This helps to identify high-risk areas and anticipate needs. It can provide rapid data analysis for decision support and optimize emergency vehicle routes. Examples include managing ventilator distribution during COVID-19 and integrating flood data after Hurricane Harvey.

Infrastructure Planning: AI can simulate future climate scenarios to help design resilient infrastructure, such as flood-resistant buildings and smarter power grids. After Hurricane Maria, AI prioritized power grid repair in Puerto Rico.

Challenges

Despite its immense potential, AI in climate resilience faces challenges. One key limitation for purely data-driven AI models is their struggle to forecast “gray swan” events (unprecedented events not in training data).

  • Computational Power and Cost: Training large AI models is computationally intensive. However, once trained, they are often hundreds to thousands of times faster and more energy-efficient for forecasting than traditional models.
  • Data Biases and Scarcity: AI models rely heavily on data quality and representation. Geographic, temporal, and variable biases can lead to skewed predictions, especially in data-poor regions. A lack of sufficient labeled data for rare extreme events is also an obstacle.
  • Limitations for Unprecedented Events: Purely data-driven AI models struggle to forecast “gray swan” events that have no precedent in their training data. This is because they cannot inherently “understand” underlying physics to extrapolate to novel extremes.
  • Ethical and Societal Considerations: Concerns include unequal access to crucial weather information, over-reliance on automated systems without human oversight, and privacy issues from personal data collection.

The Path Forward

  • More Efficient & Explainable Models (XAI): Continued XAI development is crucial for transparent and trustworthy AI predictions.
  • Global Data Infrastructure and Open Access: Investing in robust, globally representative data collection and promoting open data will democratize advanced forecasting.
  • International Collaboration: Fostering collaboration among scientists, AI researchers, and policymakers is critical for more scalable AI solutions.
  • Responsible and Equitable AI Deployment: Establishing policy frameworks ensures AI applications adhere to principles of equity, transparency, and sustainability.

By addressing these challenges, AI can be a powerful ally against extreme weather, enabling a more proactive and adaptive approach to climate resilience. 

References