Accurate hydrological understanding and water cycle prediction are crucial for addressing
scientific and societal challenges associated with the management of water resources …

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 to …

Modeling environmental ecosystems is critical for the sustainability of our planet, but is
extremely challenging due to the complex underlying processes driven by interactions …

Ecosystem services, such as carbon sequestration, biodiversity, and climate regulation, play
essential roles in combating climate change. Projection of ecosystem dynamics under …

When dealing with data from distinct locations, machine learning algorithms tend to
demonstrate an implicit preference of some locations over the others, which constitutes …

This paper presents an overview of scientific modeling and discusses the complementary
strengths and weaknesses of ML methods for scientific modeling in comparison to process …

This paper proposes a meta-transfer-learning method for predicting daily maximum water
temperature in stream networks with explicit modeling of extreme events. Accurate …

This paper introduces a\textit {Process-Guided Learning (Pril)} framework that integrates
physical models with recurrent neural networks (RNNs) to enhance the prediction of …

Estimating human mobility is an important task in diverse societal domains, including public
health, public safety, transportation, agriculture, environmental science, etc. This thesis …