Streamflow prediction with limited spatially-distributed input data

Section 1: Publication

Publication Type

Authorship

Gauch, M., Mai, J., Gharari, S., & Lin, J.

Title

Streamflow prediction with limited spatially-distributed input data

Year

2019

Publication Outlet

In Proceedings of the NeurIPS 2019 Workshop on Tackling Climate Change with Machine Learning

DOI

ISBN

ISSN

Citation

Gauch, M., Mai, J., Gharari, S., & Lin, J. (2019b). Streamflow prediction with limited spatially-distributed input data. In Proceedings of the NeurIPS 2019 Workshop on Tackling Climate Change with Machine Learning. https://cs.uwaterloo.ca/~jimmylin/publications/Gauch_etal_NeurIPS2019workshop.pdf

Abstract

Climate change causes more frequent and extreme weather phenomena across the globe. Accurate streamflow prediction allows for proactive and mitigative action in some of these events. As a first step towards models that predict streamflow in watersheds for which we lack ground truth measurements, we explore models that work on spatially-distributed input data. In such a scenario, input variables are more difficult to acquire, and thus models have access to limited training data. We present a case study focusing on Lake Erie, where we find that tree-based models can yield more accurate predictions than both neural and physically-based models.

Plain Language Summary