Data-driven models for accurate groundwater level prediction and their practical significance in groundwater management
Data-driven models for accurate groundwater level prediction and their practical significance in groundwater management
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准确预测地下水位的数据驱动模型及其在地下水管理中的实际意义
DOI：
10.1016/j.jhydrol.2022.127630
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发表时间：
2022-02
影响因子：
6.4
通讯作者：
Zhengqiu Yang
中科院分区：
文献类型：
--
作者：
Jianchong Sun;Litang Hu;D;an Li;Kangning Sun;Zhengqiu Yang
近年来，地下水资源的过度开采及其精细化管理因其带来的一系列严重的环境地质问题而受到世界各国的广泛关注。目前，准确预测地下水位（GWL）是跨尺度地下水有效管理的重要问题。在这项研究中，三个常用的数据驱动模型，即自回归积分移动平均（ARIMA），反向传播人工神经网络（BP-ANN）和长短期记忆（LSTM），建立在5个不同的水文地质特性的区域，以探讨该模型的精度预测GWL在中国北方平原的月和日尺度。所开发的模型进行了评估的Nash-Sutcliffe效率系数（NSE）和均方根误差（RMSE）。结果表明，LSTM模型在训练期间每个区域的NSE大于0.76且RMSE小于1.15 m的月时间尺度下性能最佳，并且在局部区域的NSE大于0.9且RMSE小于0.55 m的日时间尺度下表现出良好的性能。同时，使用面向对象的空间统计（O2 S2）方法估计LSTM模型的提取概率的时空分布。结果表明：2003 - 2010年，累积水位下降大于10 m的概率大于0.7，而2011 - 2014年，累积水位下降小于0.3的概率主要集中在水源地。从2015年到2018年，研究区的GWL普遍上升，但由于持续的地下水开采，V区水位下降超过5米的概率超过0.8。这项研究制定了一个框架，开发有效的数据驱动模型，用于预测跨尺度的GWL，有可能帮助地下水管理。
The overexploitation of groundwater resource and its delicacy management has gained increasing attentions in recent years worldwide because of causing a series of serious environmental and geological problems. Currently, accurately predicting the groundwater level (GWL) is an important issue in effective groundwater management across scales. In the present study, three popularly-used data-driven models, which are an autoregressive integrated moving average (ARIMA), a back-propagation artificial neural network (BP-ANN) and long short-term memory (LSTM), were established in five zones with different hydrogeological properties to explore the model’s accuracy in predicting the GWL at monthly and daily scales in a Northern Plain in China. The developed models were evaluated by both the Nash-Sutcliffe efficiency coefficient (NSE) and root mean square error (RMSE). The results indicate that the performance of the LSTM model is best at monthly time scales with the NSEs greater than 0.76 and RMSEs smaller than 1.15 m in each zone during the training period and demonstrate a good performance at daily time scales with the NSEs greater than 0.9 and the RMSEs smaller than 0.55 m at a local area. Meanwhile, the tempo-spatial distribution of the probability of drawdowns from the LSTM model was estimated by using the object-oriented spatial statistical (O2S2) method. The results show that cumulative drawdowns greater than 10 m are mainly concentrated in water source areas, with probabilities over 0.7 from 2003 to 2010 and declining to less than 0.3 from 2011 to 2014. The GWL rose generally in the study area from 2015 to 2018, but the probability of a drawdown with more than 5 m exceeded 0.8 in Zone V because of continuing groundwater exploitation. This study formulates a framework on developing effective data-driven models for predicting the GWL across scales which have the potential to aid groundwater management.
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影响因子：
3.4
作者：
张梦琳;胡立堂;Yao LL;Yin WJ
通讯作者：
Yin WJ
影响因子：
6
作者：
Najah, A.;El-Shafie, A.;El-Shafie, Amr H.
通讯作者：
El-Shafie, Amr H.
影响因子：
3.4
作者：
A. Menafoglio;P. Secchi
通讯作者：
A. Menafoglio;P. Secchi
影响因子：
6.3
作者：
Kratzert, Frederik;Klotz, Daniel;Herrnegger, Mathew
通讯作者：
Herrnegger, Mathew
影响因子：
1.8
作者：
Mueller, Juliane;Park, Jangho;Agarwal, Deborah
通讯作者：
Agarwal, Deborah