Robust Inverse Framework using Knowledge-guided Self-Supervised Learning: An application to Hydrology. (arXiv:2109.06429v2 [cs.LG] UPDATED)

Machine Learning is beginning to provide state-of-the-art performance in a
range of environmental applications such as streamflow prediction in a
hydrologic basin. However, building accurate broad-scale models for streamflow
remains challenging in practice due to the variability in the dominant
hydrologic processes, which are best captured by sets of process-related basin
characteristics. Existing basin characteristics suffer from noise and
uncertainty, among many other things, which adversely impact model performance.
To tackle the above challenges, in this paper, we propose a …

application arxiv framework hydrology knowledge learning lg self-supervised learning supervised learning