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

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.

https://cs.uwaterloo.ca/~jimmylin/publications/Gauch_etal_NeurIPS2019workshop.pdf