iHARP's Emam Hossain Presents at AMS!

iHARP’s Emam Hossain, UMBC PhD candidate, presented his paper "Incorporating Causality with Deep Learning in Predicting Short-term and Seasonal Sea Ice" at the American Meteorological Society’s 23rd Conference on Artificial Intelligence for Environmental Science, 104th AMS Annual Meeting, 2024.  Emam’s research highlights the importance of Arctic sea ice (ASI) in controlling global warming and the concerning trend of its decline. It underscores the need for accurate forecasting to address various socio-economic issues. While current correlation-based machine learning (ML) models have limited capabilities, causal models offer promise by considering cause-and-effect relationships between atmospheric variables. The study aims to predict ASI using causal features identified by Granger Causality (GC) and PCMCI+ algorithms. Results show that models trained on causal features outperform those trained on correlated features. The study seeks to develop a deep learning model incorporating causality for both short-term and long-term sea ice prediction, with potential for further improvement by considering exact time lags identified by the causal algorithm.

We congratulate Emam for presenting and representing at AMS to share his research and knowledge on data science, polar science, and data analysis!

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