Robust Inverse Framework using Knowledge-guided Self-Supervised Learning: An application to Hydrology
This addresses the problem of noisy and uncertain basin characteristics in hydrology for improving streamflow prediction, representing a novel method for a known bottleneck.
The paper tackles the challenge of building accurate broad-scale streamflow models by proposing a Knowledge-guided Self-Supervised Learning (KGSSL) inverse framework to extract robust system characteristics from data, achieving up to 16% improvement in reconstructing characteristics and 35% better performance when using inferred characteristics compared to baselines.
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 novel Knowledge-guided Self-Supervised Learning (KGSSL) inverse framework to extract system characteristics from driver and response data. This first-of-its-kind framework achieves robust performance even when characteristics are corrupted. We show that KGSSL achieves state-of-the-art results for streamflow modeling for CAMELS (Catchment Attributes and MEteorology for Large-sample Studies) which is a widely used hydrology benchmark dataset. Specifically, KGSSL outperforms other methods by up to 16 \% in reconstructing characteristics. Furthermore, we show that KGSSL is relatively more robust to distortion than baseline methods, and outperforms the baseline model by 35\% when plugging in KGSSL inferred characteristics.