Aquifer-differentiated process-guided GRU for multi-timescale groundwater-level prediction
Abstract
Groundwater-level prediction supports water-resource management in overexploited aquifers, yet conventional data-driven models often struggle to represent pumping disturbances and contrasting shallow- and deep-aquifer responses. Using groundwater levels, meteorological variables, and agricultural pumping data from four shallow and four deep wells in Guantao County, North China Plain, we developed an aquifer-differentiated process-guided gated recurrent unit model for daily and dekadal (10-day) prediction. Compared with a conventional gated recurrent unit model, the process-guided model reduced group-mean root-mean-square error by 7.2% and 6.1% for shallow and deep wells at the daily scale and by 4.0% and 2.1% at the dekadal scale, respectively. Deep-well performance depended on the pumping-response window, with 60-day averaged pumping providing a stable configuration. These incremental gains show that incorporating pumping disturbances and aquifer-specific response differences as process guidance can improve groundwater-level representation and provide a modeling approach for groundwater management in agricultural abstraction areas.
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BibTeX
@article{xie2026aquiferdifferentiated,
title = {{Aquifer-differentiated process-guided GRU for multi-timescale groundwater-level prediction}},
author = {Xiandong Xie and Baizhong Yan and Tian Wang and Chuanlong Han and Cong Wei and Yanliang Zhai and Jialong Lv and Mingrong Zhang},
journal = {iScience},
year = {2026},
month = oct,
doi = {10.1016/j.isci.2026.117832},
url = {https://www.cell.com/iscience/fulltext/S2589-0042(26)03213-X},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Aquifer-differentiated process-guided GRU for multi-timescale groundwater-level prediction
AU - Xiandong Xie
AU - Baizhong Yan
AU - Tian Wang
AU - Chuanlong Han
AU - Cong Wei
AU - Yanliang Zhai
AU - Jialong Lv
AU - Mingrong Zhang
PY - 2026
DA - 2026/10/09
JO - iScience
DO - 10.1016/j.isci.2026.117832
UR - https://www.cell.com/iscience/fulltext/S2589-0042(26)03213-X
AB - Groundwater-level prediction supports water-resource management in overexploited aquifers, yet conventional data-driven models often struggle to represent pumping disturbances and contrasting shallow- and deep-aquifer responses. Using groundwater levels, meteorological variables, and agricultural pumping data from four shallow and four deep wells in Guantao County, North China Plain, we developed an aquifer-differentiated process-guided gated recurrent unit model for daily and dekadal (10-day) prediction. Compared with a conventional gated recurrent unit model, the process-guided model reduced group-mean root-mean-square error by 7.2% and 6.1% for shallow and deep wells at the daily scale and by 4.0% and 2.1% at the dekadal scale, respectively. Deep-well performance depended on the pumping-response window, with 60-day averaged pumping providing a stable configuration. These incremental gains show that incorporating pumping disturbances and aquifer-specific response differences as process guidance can improve groundwater-level representation and provide a modeling approach for groundwater management in agricultural abstraction areas.
SN - 2589-0042
ER -