arXiv Machine Learning By Hancheng Ren, Gang Zhao, Shuo Wang, Louise Slater, Dai Yamazaki, Shu Liu, Jingfang Fan, Xueying Li, Shibo Cui, Ziming Yu, Shengyu Kang, Depeng Zuo, Dingzhi Peng, Zongxue Xu, Bo Pang

Topology enables learning-based hydrodynamic prediction of the global river system

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arXiv Machine Learning
Jun 11

LakeFM: Toward a Foundation Model for Aquatic Ecosystems Using Irregular Multivariate Multi-depth Time Series Data

arXiv:2606. 11268v1 Announce Type: new Abstract: Understanding and forecasting lake dynamics is critical for monitoring water quality and ecosystem health across lakes and reservoirs.

By Abhilash Neog, Sepideh Fatemi, Medha Sawhney, Kazi Sajeed Mehrab, Aanish Pradhan, Bennett J. McAfee, Emma Marchisin, Arka Daw, Robert Ladwig, Cayelan C. Carey, Paul Hanson, Anuj Karpatne
Hugging Face Trending Papers
Sep 10

A Dataset and Model for Imputing Water Surface Elevation on a Large and Extremely Sparse Spatiotemporal Graph

The paper introduces AmazonSWE, a dataset covering over 19,000 river sections in the Amazon basin for 10 years, combining satellite altimetry and in‑situ gauge data to enable large‑scale spatiotemporal graph imputation. The authors highlight the extreme sparsity of observations—less than 1% of sections per day—and the directed acyclic topology of river networks, which challenge existing imputation methods. They propose a bidirectional selective state‑space model that samples connected subgraphs and uses topology‑aware positional encodings, achieving 18–39% lower RMSE than the current state‑of‑the‑art SWOT‑based approach while providing predictions for all river sections.

arXiv Machine Learning
Jun 18

INDEQS: Informed Neural controlled Differential EQuationS

arXiv:2606. 19138v1 Announce Type: new Abstract: Neural Controlled Differential Equations (NCDE) provide a powerful continuous-time framework for forecasting time series, but standard graph-based extensions typically learn spatial structure purely from data, even in settings where a directed graph structure is known a priori.

By Michael Detzel, Gabriel Nobis, Kristiyan Blagov, Juri Schubert, Jackie Ma, Wojciech Samek