Knowledge-guided Self-supervised Learning for estimating River-Basin
Characteristics
- URL: http://arxiv.org/abs/2109.06429v1
- Date: Tue, 14 Sep 2021 04:38:23 GMT
- Title: Knowledge-guided Self-supervised Learning for estimating River-Basin
Characteristics
- Authors: Rahul Ghosh, Arvind Renganathan, Ankush Khandelwal, Xiaowei Jia, Xiang
Li, John Neiber, Chris Duffy, Vipin Kumar
- Abstract summary: We present an inverse model that uses a knowledge-guided self-supervised learning algorithm to infer basin characteristics.
We evaluate our model on the the CAMELS dataset and the results validate its ability to reduce measurement uncertainty.
- Score: 21.692056111111476
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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