Scholarlink

<p>Groundwater level (GWL) is a key indicator used to accurately assess groundwater resources and form the foundation for effective groundwater management. This paper integrates a Gate Recurrent Unit (GRU) model with a Multi-head Self-attention mechanism (MSAM-GRU) to simulate GWLs in both confined and unconfined aquifers simultaneously. The model innovatively captures the lag times between GWLs in the unconfined aquifer and precipitation, as well as between GWLs in the confined aquifer and the upper aquifer. We have assessed the effectiveness of the proposed model using a case study in the Beijing Plain, China from January 2005 to December 2020. With the consideration of lag times, the results indicated that the MSAM-GRU model exhibits a maximum 67% and 73% reduction in <i>RMSE</i> compared to the Attention mechanism-GRU (AM-GRU) and GRU model, respectively. MSAM-GRU model exhibited a 31% reduction in <i>RMSE</i> and a 0.12 increase in <i>R</i><sup>2</sup> compared to the same model that do not account for lag time. In Region I, the shortest lag time of GWL in the unconfined aquifer was two months, while that in the confined aquifer was three months, indicating a longer delayed response in the confined aquifer. MSAM-GRU model considering lag time, was then applied to simulate the GWLs in the unconfined aquifer under different scenarios and to analyze whether GWL fluctuations affect subway operations. The simulation results showed that under the scenario 1, the GWL in the unconfined aquifer would rise above the depth of subway station floor, threatening the operation of subways. This study can provide reliable technical support for the accurate simulation of GWLs in multi-aquifer systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Simulating Groundwater Levels Responses to Precipitation and Withdrawal: A Lag-time Deep Learning Model

  • 期刊论文

  • 发表日期：

    2026年2月13日

查看全文
  • Shuai Li,
  • Lin Zhu,
  • Lei Gao,
  • Huili Gong,
  • Xiaojuan Li,
  • Xiaosi Su

摘要

Groundwater level (GWL) is a key indicator used to accurately assess groundwater resources and form the foundation for effective groundwater management. This paper integrates a Gate Recurrent Unit (GRU) model with a Multi-head Self-attention mechanism (MSAM-GRU) to simulate GWLs in both confined and unconfined aquifers simultaneously. The model innovatively captures the lag times between GWLs in the unconfined aquifer and precipitation, as well as between GWLs in the confined aquifer and the upper aquifer. We have assessed the effectiveness of the proposed model using a case study in the Beijing Plain, China from January 2005 to December 2020. With the consideration of lag times, the results indicated that the MSAM-GRU model exhibits a maximum 67% and 73% reduction in RMSE compared to the Attention mechanism-GRU (AM-GRU) and GRU model, respectively. MSAM-GRU model exhibited a 31% reduction in RMSE and a 0.12 increase in R2 compared to the same model that do not account for lag time. In Region I, the shortest lag time of GWL in the unconfined aquifer was two months, while that in the confined aquifer was three months, indicating a longer delayed response in the confined aquifer. MSAM-GRU model considering lag time, was then applied to simulate the GWLs in the unconfined aquifer under different scenarios and to analyze whether GWL fluctuations affect subway operations. The simulation results showed that under the scenario 1, the GWL in the unconfined aquifer would rise above the depth of subway station floor, threatening the operation of subways. This study can provide reliable technical support for the accurate simulation of GWLs in multi-aquifer systems.