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Accelerating Domain-aware Deep Learning Models with Distributed Training
We perform extensive experiments on a dataset of 23 watersheds in a northern state of the U. S. and present our findings.
Transfer Learning Approaches for Knowledge Discovery in Grid-based Geo-Spatiotemporal Data
Extracting and meticulously analyzing geo-spatiotemporal features is crucial to recognize intricate underlying causes of natural events, such as floods.
HydroDeep -- A Knowledge Guided Deep Neural Network for Geo-Spatiotemporal Data Analysis
Due to limited evidence and complex causes of regional climate change, the confidence in predicting fluvial floods remains low.
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