气候变化对河南华北平原地下水含水层影响的预测

IF 5.7 3区 环境科学与生态学 Q1 WATER RESOURCES
Rabia Dars, Jianhua Ping, Xuemei Mei, Chun Chen, Abdul Raheem Shahzad, Joshua Mahwa, Muhammad Afzal Jamali
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引用次数: 0

摘要

长期监测GWL对于了解当前全球变化背景下地下水资源的波动至关重要。利用双向长短期记忆(BidLSTM)和门控循环单元(GRU)两种深度学习模型,分析了气候变化对河南省华北平原GWL的影响。这些模型使用1980年至2015年的数据集预测了该地区85口监测井的GWL月度变化。为了验证和评估,使用训练集(1980-2015)对两个模型进行了定量校准,以预测2016年至2100年的GWL。数据集被划分,80%分配给训练,20%分配给测试。长城航空AHP3的结果解释,嗯,下降到120年的1980由于降水量减少57毫米和62毫米,而温度保持在10°C到2070年,在郑州长城航空和Keifing地区下降了98在1980年代尽管降水72毫米上升和Et 60毫米,由于充电不足,到2100年,长城航空预计将达到140米,由于气候变化,包括温度增加到17°C。结果表明,受降水、气温和地表等因素的影响,GWL发生了显著变化。训练后的模型性能良好,预测误差分别为0.0350、0.0346 m,均方根误差(RMSE)分别为0.1870、0.1860 m。BidLSTM模型通过准确预测gwl,有助于确保地下水资源的可持续、高效利用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting climate change impacts on groundwater aquifer levels in the Henan North China Plain

Monitoring GWL over extended periods is crucial for comprehending the fluctuations of groundwater resources in the present context for ongoing global changes. This study analyzed the effects of climate variations on the GWL in Henan Province North China Plain using two deep-learning models Bidirectional Long Short-Term Memory (BidLSTM) and Gated Recurrent Unit (GRU). These models predicted monthly variations in GWL at 85 monitoring wells across the area using a dataset from 1980 to 2015. For validation and evaluation, both models were quantitatively calibrated using training set (1980–2015) to predict GWL from 2016 to 2100. The dataset was partitioned, with 80% allocated for training and 20% for testing. The result interpreted that in AHP3 well, GWL declined to 120 m in 1980 due to reduced precipitation 57 mm and Et 62 mm, while temperature stayed at 10 °C as of 2070, In the Zhengzhou and Keifing regions GWL declined by 98 m in the 1980 s despite rising precipitation 72 mm and Et 60 mm, due to insufficient recharge by 2100, GWL is expected to reach 140 m, driven by climate changes, including a temperature increase to 17 °C. The results indicated significant changes with the effect of precipitation, significant increase in temperature and surface Et. Anthropogenic activity also impacted GWL in the area. The trained models demonstrated good performance, with a prediction error of 0.0350, 0.0346 m, and the root mean square error (RMSE) was recorded at 0.1870, 0.1860 m. By accurately predicting GWLs, the BidLSTM model can help ensure that groundwater resources are used sustainably and efficiently.

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来源期刊
Applied Water Science
Applied Water Science WATER RESOURCES-
CiteScore
9.90
自引率
3.60%
发文量
268
审稿时长
13 weeks
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