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arXiv Machine Learning By Ziqi Wang, Hailiang Zhao, Cheng Bao, Daojiang Hu, Wenzhuo Qian, Shuiguang Deng

Learning Intrinsic Water-Quality Dynamics with Rainfall for Data-Driven Forecasting

Read the original on arXiv Machine Learning →

The paper introduces RaiNet, a data‑driven model that jointly learns multiscale water‑quality dynamics and station‑specific rainfall effects. It uses LocTrend to capture irregular water‑quality patterns, constructs station‑oriented rainfall events from gridded precipitation, and applies XGateFusion for lag‑aware fusion across scales. Experiments on three new multimodal datasets show RaiNet surpasses existing time‑series, water‑quality, diffusion‑based, and spatiotemporal models by over 20%, with each module contributing uniquely to performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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