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arXiv · 2508.08279

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

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

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Ziqi Wang, Hailiang Zhao, Cheng Bao, Daojiang Hu, Wenzhuo Qian, Shuiguang Deng. 2025-08-01. Learning Intrinsic Water-Quality Dynamics with Rainfall for Data-Driven Forecasting. https://arxiv.org/abs/2508.08279

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