1 citations · 4 across the 27 of their papers we have counts for
7 papers · 1 filter
Physics-Guided Fair Graph Sampling for Water Temperature Prediction in River Networks
Erhu He, Declan Kutscher, Yiqun Xie +4
This work introduces a novel graph neural networks (GNNs)-based method to predict stream water temperature and reduce model bias across locations of different income and education…
Adaptive Process-Guided Learning: An Application in Predicting Lake DO Concentrations
Runlong Yu, Chonghao Qiu, Robert Ladwig +4
This paper introduces a \textit{Process-Guided Learning (Pril)} framework that integrates physical models with recurrent neural networks (RNNs) to enhance the prediction of dissolv…
When are Foundation Models Effective? Understanding the Suitability for Pixel-Level Classification Using Multispectral Imagery
Yiqun Xie, Zhihao Wang, Weiye Chen +7
Foundation models, i.e., very large deep learning models, have demonstrated impressive performances in various language and vision tasks that are otherwise difficult to reach using…
LITE: Modeling Environmental Ecosystems with Multimodal Large Language Models
Haoran Li, Junqi Liu, Zexian Wang +3
The modeling of environmental ecosystems plays a pivotal role in the sustainable management of our planet. Accurate prediction of key environmental variables over space and time ca…
Evolution-based Feature Selection for Predicting Dissolved Oxygen Concentrations in Lakes
Runlong Yu, Robert Ladwig, Xiang Xu +4
Accurate prediction of dissolved oxygen (DO) concentrations in lakes requires a comprehensive study of phenological patterns across ecosystems, highlighting the need for precise se…
Referee-Meta-Learning for Fast Adaptation of Locational Fairness
Weiye Chen, Yiqun Xie, Xiaowei Jia +4
When dealing with data from distinct locations, machine learning algorithms tend to demonstrate an implicit preference of some locations over the others, which constitutes biases t…
SimFair: Physics-Guided Fairness-Aware Learning with Simulation Models
Zhihao Wang, Yiqun Xie, Zhili Li +4
Fairness-awareness has emerged as an essential building block for the responsible use of artificial intelligence in real applications. In many cases, inequity in performance is due…