Articles | Volume 30, issue 17
https://doi.org/10.5194/hess-30-5647-2026
https://doi.org/10.5194/hess-30-5647-2026
Research article
 | 
08 Sep 2026
Research article |  | 08 Sep 2026

Hydrochemistry and modeling nitrate concentration in farmland groundwater under different hydrological seasons by integrating hybrid quantum-classical ML, virtual sample generation and AlphaEarth Foundation

Junjie Xu, Xin Wei, Yilei Yu, Lihu Yang, Yuanzheng Zhai, Cuicui Lv, and Xianfang Song

Data sets

sherlockjjobs/Quantum-RF-framework: Quantum-RF-framework (Version groundwater-nitrate-prediction) J. Xu https://doi.org/10.5281/zenodo.22329075

Model code and software

sherlockjjobs/Quantum-RF-framework: Quantum-RF-framework (Version groundwater-nitrate-prediction) J. Xu https://doi.org/10.5281/zenodo.22329075

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Short summary
Nitrate from farms and villages often seeps into groundwater, threatening water safety. We studied a farming area in northern China across dry, wet, and normal seasons to track and predict nitrate. Nitrate peaked in the dry season due to evaporation, and manure plus household wastewater supplied three quarters of it. Combining artificial data samples with advanced learning methods, we predicted nitrate accurately. This helps officials find pollution hotspots cheaply and protect rural water.
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