Accurate Hydrologic Modeling Using Less Information
This addresses data scarcity in hydrologic modeling for regions with limited measurements, though it is incremental as it builds on existing joint models.
The paper tackled the problem of rainfall-runoff modeling in hydrology by replacing location-specific attributes with a data-driven learned embedding, achieving state-of-the-art results with less information.
Joint models are a common and important tool in the intersection of machine learning and the physical sciences, particularly in contexts where real-world measurements are scarce. Recent developments in rainfall-runoff modeling, one of the prime challenges in hydrology, show the value of a joint model with shared representation in this important context. However, current state-of-the-art models depend on detailed and reliable attributes characterizing each site to help the model differentiate correctly between the behavior of different sites. This dependency can present a challenge in data-poor regions. In this paper, we show that we can replace the need for such location-specific attributes with a completely data-driven learned embedding, and match previous state-of-the-art results with less information.