March 29, 2024, 4:42 a.m. | Runlong Yu, Robert Ladwig, Xiang Xu, Peijun Zhu, Paul C. Hanson, Yiqun Xie, Xiaowei Jia

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.18923v1 Announce Type: cross
Abstract: Predicting dissolved oxygen (DO) concentrations in north temperate lakes requires a comprehensive study of phenological patterns across various ecosystems, which highlights the significance of selecting phenological features and feature interactions. Process-based models are limited by partial process knowledge or oversimplified feature representations, while machine learning models face challenges in efficiently selecting relevant feature interactions for different lake types and tasks, especially under the infrequent nature of DO data collection. In this paper, we propose a …

abstract arxiv cognitive cs.ai cs.lg cs.ne ecosystems evolution feature features highlights interactions knowledge nature patterns process significance study type

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