Machine Learning is being extensively used in hydrology, especially streamflow prediction of basins/watersheds. Basin characteristics are essential for modeling the rainfall-runoff response of these watersheds and therefore data-driven methods must take into account this ancillary characteristics data. However there are several limitations, namely uncertainty in the measured characteristics, partially missing characteristics for some of the basins or unknown characteristics that may not be present in the known measured set. In this paper we present an inverse model that uses a knowledge-guided self-supervised learning algorithm to infer basin characteristics using the meteorological drivers and streamflow response data. We evaluate our model on the the CAMELS dataset and the results validate its ability to reduce measurement uncertainty, impute missing characteristics, and identify unknown characteristics.

本文提出了一种可解决环境应用和流域特征多变等问题，基于知识引导的自监督学习KGSSL框架，并对基于流域属性和气象数据的CAMELS水文基准数据集进行流量建模，并表明在从驱动器和响应数据提取系统特征时，KGSSL可以更加鲁棒.

应用知识引导自监督学习的强健反演框架：以水文学为例