This paper proposes a graph-based meta learning approach to separately predict water
quantity and quality variables for river segments in stream networks. Given the …

Additive manufacturing (AM) provides a data-rich environment for collecting a variety of
process data. These crucial data can be used to develop effective machine learning (ML) …

Despite improvements in safe water and sanitation services in low-income countries, a
substantial proportion of the population in Africa still does not have access to these essential …

Water environments (eg, oceans, lakes, and rivers) are important carbon sinks and sources
and contribute to the carbon cycle of the earth's ecosystem. Machine learning provides a …

Accurate modelling of the scroll expander is essential for efficiency analysis and optimal
control. In this study, we propose a mechanism-incorporated adaptive-network-based fuzzy …

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 …

With the recent rapid advances of revolutionary AI models such as ChatGPT, foundation
models have become a main topic for the discussion of future AI. Despite the excitement, the …

There is a new trend in marine hydrometeorology (MHM) that calls for novel solutions on
massive multi-source and heterogeneous spatiotemporal data sets. Traditionally, the …

Physics-based groundwater flow equations are powerful tools for water resource
assessment under different hydrological and climatic conditions. How these conditions affect …

Contrastive learning techniques make it possible to pretrain a general model in a self-
supervised paradigm using a large number of unlabeled remote sensing images. The core …