88 citations · 284 across the 10 of their papers we have counts for
10 papers
Foundation Models for Environmental Science: A Survey of Emerging Frontiers
Runlong Yu, Shengyu Chen, Yiqun Xie +3
Modeling environmental ecosystems is essential for effective resource management, sustainable development, and understanding complex ecological processes. However, traditional data…
Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models
Jared D. Willard, Fabio Ciulla, Helen Weierbach +2
The prediction of streamflows and other environmental variables in unmonitored basins is a grand challenge in hydrology. Recent machine learning (ML) models can harness vast datase…
Time Series Predictions in Unmonitored Sites: A Survey of Machine Learning Techniques in Water Resources
Jared D. Willard, Charuleka Varadharajan, Xiaowei Jia +1
Prediction of dynamic environmental variables in unmonitored sites remains a long-standing challenge for water resources science. The majority of the world's freshwater resources h…
Mini-Batch Learning Strategies for modeling long term temporal dependencies: A study in environmental applications
Shaoming Xu, Ankush Khandelwal, Xiang Li +9
In many environmental applications, recurrent neural networks (RNNs) are often used to model physical variables with long temporal dependencies. However, due to mini-batch training…
Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning
Jared D. Willard, Jordan S. Read, Alison P. Appling +3
Most environmental data come from a minority of well-monitored sites. An ongoing challenge in the environmental sciences is transferring knowledge from monitored sites to unmonitor…
Physics-Guided Recurrent Graph Networks for Predicting Flow and Temperature in River Networks
Xiaowei Jia, Jacob Zwart, Jeffrey Sadler +8
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 t…
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems
Jared Willard, Xiaowei Jia, Shaoming Xu +2
There is a growing consensus that solutions to complex science and engineering problems require novel methodologies that are able to integrate traditional physics-based modeling ap…
Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles
Xiaowei Jia, Jared Willard, Anuj Karpatne +4
Physics-based models of dynamical systems are often used to study engineering and environmental systems. Despite their extensive use, these models have several well-known limitatio…
Physics Guided Recurrent Neural Networks For Modeling Dynamical Systems: Application to Monitoring Water Temperature And Quality In Lakes
Xiaowei Jia, Anuj Karpatne, Jared Willard +5
In this paper, we introduce a novel framework for combining scientific knowledge within physics-based models and recurrent neural networks to advance scientific discovery in many d…
Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles
Xiaowei Jia, Jared Willard, Anuj Karpatne +4
This paper proposes a physics-guided recurrent neural network model (PGRNN) that combines RNNs and physics-based models to leverage their complementary strengths and improve the mo…