no code implementations • 11 Feb 2022 • Xiaowei Jia, Shengyu Chen, Yiqun Xie, HaoYu Yang, Alison Appling, Samantha Oliver, Zhe Jiang
However, the information of released water flow is often not available for many reservoirs, which makes it difficult for data-driven models to capture the impact to downstream river segments.
no code implementations • 11 Oct 2021 • Shengyu Chen, Alison Appling, Samantha Oliver, Hayley Corson-Dosch, Jordan Read, Jeffrey Sadler, Jacob Zwart, Xiaowei Jia
In this paper, we propose a heterogeneous recurrent graph model to represent these interacting processes that underlie stream-reservoir networks and improve the prediction of water temperature in all river segments within a network.
no code implementations • 27 Oct 2020 • Xiaowei Jia, Beiyu Lin, Jacob Zwart, Jeffrey Sadler, Alison Appling, Samantha Oliver, Jordan Read
In this paper, we develop a real-time active learning method that uses the spatial and temporal contextual information to select representative query samples in a reinforcement learning framework.
no code implementations • 26 Sep 2020 • Xiaowei Jia, Jacob Zwart, Jeffrey Sadler, Alison Appling, Samantha Oliver, Steven Markstrom, Jared Willard, Shaoming Xu, Michael Steinbach, Jordan Read, Vipin Kumar
This paper proposes a physics-guided machine learning approach that combines advanced machine learning models and physics-based models to improve the prediction of water flow and temperature in river networks.