Multimodal Machine Learning and Climate Change Adaptation

Climate change is escalating the frequency and severity of natural disasters worldwide, necessitating urgent societal adaptation. In this talk, I present a multimodal machine learning (ML) framework designed to predict natural disasters. Traditionally, weather forecasting has depended on dynamical equations for over a century. However, recent advancements in artificial intelligence are revolutionizing this domain. The innovative multimodal ML framework leverages processing techniques from computer vision, natural language processing, time series signal processing techniques to integrate various data types, such as satellite imagery, textual information, and tabular data, to generate both short-term and long-term forecasts. Our first case study demonstrates that, for 24-hour hurricane forecasting, our ML models achieve results that are competitive with those produced by established national weather forecasting agencies. In our second case study, we explore the potential to create global models with a multi-year scope for assessing flood risks. Artificial intelligence will fundamentally change the way our interaction with weather, and these ML-driven risk assessments will have profound impacts on urban planning, infrastructure investment, renewable energy planning, and insurance policy.

Speaker: Cynthia Zeng, Massachusets Institute of Technology

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