big-data-lab-umbc / sea-ice-prediction

This repository enlists some of the ongoing research projects at Big Data Analytics Lab for Arctic Sea Ice Forecasting. We have provided code for multiple such projects including:

1. Arctic Sea Ice Forecasting using Attention-based Ensemble LSTM.[1]
2. Comparative Analysis of Probabilistic Machine Learning models for predicting Arctic Sea Ice.[2]
3. A Hands-on tutorial for beginners to practice state-of-the-art AI techniques to forecast Arctic Sea Ice.
4. Physics-guided Machine Learning for Sea Ice Forecasting.
5. MT-IceNet - A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting [3]

For details on similar projects, visit our BDAL lab.

References:

1. Ali, S., Huang, Y., Huang, X., Wang, J. (2021). Sea Ice Forecasting using Attention-based Ensemble LSTM. Tackling Climate Change through Machine Learning Workshop at ICML 2021 (also at:arXiv preprint arXiv:2108.00853.)
2. Ali, S., Mostafa, S.A.M., Li, X., Khanjani, S., Wang, J., Foulds, J., Janeja, J. Benchmarking Probabilistic Machine Learning Models for Arctic Sea Ice. In 2022 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2022), IEEE.
3. Ali, S., Wang, J. MT-IceNet – A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting. In 2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT 2022), IEEE/ACM.