Transfer Learning Approaches for Knowledge Discovery in Grid-based Geo-Spatiotemporal Data
This addresses the challenge of computationally efficient flood prediction for climate science, but it is incremental as it builds on existing transfer learning methods.
The paper tackles the problem of predicting regional water discharge by proposing HydroDeep, a reusable pretrained model for transferring knowledge between regions, which improves Nash-Sutcliffe efficiency by 9% to 108% and reduces time by 95% in new regions.
Extracting and meticulously analyzing geo-spatiotemporal features is crucial to recognize intricate underlying causes of natural events, such as floods. Limited evidence about hidden factors leading to climate change makes it challenging to predict regional water discharge accurately. In addition, the explosive growth in complex geo-spatiotemporal environment data that requires repeated learning by the state-of-the-art neural networks for every new region emphasizes the need for new computationally efficient methods, advanced computational resources, and extensive training on a massive amount of available monitored data. We, therefore, propose HydroDeep, an effectively reusable pretrained model to address this problem of transferring knowledge from one region to another by effectively capturing their intrinsic geo-spatiotemporal variance. Further, we present four transfer learning approaches on HydroDeep for spatiotemporal interpretability that improve Nash-Sutcliffe efficiency by 9% to 108% in new regions with a 95% reduction in time.