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

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.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2508.08279 [cs.LG]

(or arXiv:2508.08279v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2508.08279 Focus to learn more arXiv-issued DOI via DataCite

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