Creating Interpretable Data-Driven Approaches for Tropical Cyclones Forecasting
DOI:
https://doi.org/10.1609/aaai.v36i11.21583Keywords:
Tropical Cylones, Machine Learning, XAIAbstract
Tropical cyclones (TC) are extreme weather phenomena that bring heavy disasters to humans. Existing forecasting techniques contain computationally intensive dynamical models and statistical methods with complex inputs, both of which have bottlenecks in intensity forecasting, and we aim to create data-driven methods to break this forecasting bottleneck. The research goal of my PhD topic is to introduce novel methods to provide accurate and trustworthy forecasting of TC by developing interpretable machine learning models to analyze the characteristics of TC from multiple sources of data such as satellite remote sensing and observations.Downloads
Published
2022-06-28
How to Cite
Meng, F. (2022). Creating Interpretable Data-Driven Approaches for Tropical Cyclones Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12892-12893. https://doi.org/10.1609/aaai.v36i11.21583
Issue
Section
The Twenty - Seventh AAAI / SIGAI Doctoral Consortium