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

Abstract

Rainfall is an important environmental driver of water-quality variations through processes such as runoff, pollutant transport, dilution, and resuspension. Traditional mechanistic models can explicitly describe these processes but often require substantial process specification and site-specific calibration, limiting their flexibility under changing hydrological conditions. In this work, we explore a data-driven alternative by proposing RaiNet to jointly model multiscale water-quality dynamics and station-specific rainfall effects across relative lags and temporal scales. RaiNet employs LocTrend to capture irregular water-quality dynamics, constructs station-oriented rainfall events from gridded precipitation, and introduces XGateFusion for conditional lag-aware fusion across scales. We further release three real-world multimodal datasets comprising over 150,000 temporally aligned water quality observations and gridded precipitation raster images. Experiments show that RaiNet outperforms general time-series, water quality, diffusion-based, and spatiotemporal models by over 20%, while component-wise analyses confirm the distinct contribution of each module.

Authors 6

Ziqi Wang, Hailiang Zhao, Cheng Bao, Daojiang Hu, Wenzhuo Qian, Shuiguang Deng

Specification

Official page: arxiv.org/abs/2508.08279
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
Arxiv announce type: replace
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
arXiv id: 2508.08279
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
Categories: cs.LG, cs.AI
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
PDF: arxiv.org/pdf/2508.08279
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
Primary category: cs.LG
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh

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T19

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5 h ago

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Authors: Ziqi Wang, Hailiang Zhao, Cheng Bao

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Value	Valid from → to	Status	Source	Confidence	Extractor
11 Sept 2026	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperLearning Intrinsic Water-Quality Dynamics with Rainfall for Data-Driven Forecasting
New paper: Learning Intrinsic Water-Quality Dynamics with Rainfall for Data-Driven Forecasting
arxiv
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Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.LG	feed	T1· Official	4 h ago	1

Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.