ML4Earth Physics-Aware Machine Learning

Machine LearningAISocial goodNon Profit

TUM Data Science in Earth Observation



Large-scale hydrodynamic models generally rely on fixed-resolution spatial grids and model parameters as well as incurring a high computational cost. This limits their ability to forecast flood crests and issue time-critical hazard warnings accurately. In this task, we should build a fast, stable, accurate flood modelling framework that can perform at scales from large scale. Specifically, we will provide the input data and ground truth data in Pakistan flood 2022. Supervised or unsupervised methods based on machine learning methods should be designed to solve the 2-D shallow water equations. Finally, based on this model, a flood forecast model should be achieved in the event of the Pakistan flood in 2022.

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