摘要
为解决因地下水相关因素未考虑充分而导致的模型对地下水位预测不准确的问题,提出观测井的空间位置距离聚类方法、水文地质属性聚类方法和距离-属性混合聚类方法,验证观测井间连通性在地下水位预测中的重要性。设计4种模型并分别对济南岩溶水域的地下水位进行模拟和预测并与实际观测值对比。预测结果表明:考虑岩溶含水层连通性特征的联合模型ConvLSTM(convolution-long short term memory)要优于传统的长短期记忆网络模型(long short term memory, LSTM)。其中考虑距离-属性混合聚类结果的同类别井(连通性强)的模型(mix-multivariate-convolution-long short term memory, M-MV-ConvLSTM)预测结果精度最高、误差最小,其平均均方根误差约为0.457,纳什效率系数约为0.216,预测准确度高于传统的LSTM预测模型。研究成果可为岩溶水域的实时地下水位预测提供借鉴。
To address the issue of inaccuracies in groundwater level predictions due to the insufficient consideration of groundwaterrelated factors,clustering methods for observation wells based on spatial distance,hydrogeological attributes,and a hybrid of distance and attributes were proposed.The significance of inter-well connectivity in groundwater level prediction was validated.Four models were designed,which were applied to simulate and predict groundwater levels in the karst water region of Jinan and compared with actual observations.The prediction results indicate that the combined model incorporating the connectivity characteristics of karst aquifers,known as convolution-long short-term memory(ConvLSTM),outperforms the traditional long short-term memory(LSTM)model.Among the models,the mix-multivariate-convolution-long short-term memory(M-MV-ConvLSTM)model,which accounts for wells of the same category based on the hybrid distance-attribute clustering results(characterized by strong connectivity),achieves the highest prediction accuracy and the smallest error.The average root mean square error is approximately 0.457,and the Nash-Sutcliffe efficiency is approximately 0.216,demonstrating a higher prediction accuracy than the traditional LSTM model.The research results is positioned to serve as a reference for real-time groundwater level prediction in karst regions.
作者
高明
李虎
刘鑫锦
张康
韩健勇
GAO Ming;LI Hu;LIU Xin-jin;ZHANG Kang;HAN Jian-yong(School of Civil Engineering,Shandong Jianzhu University,Jinan 250101,China;Jinan Rail Transit Group Co.,Ltd.,Jinan 250014,China)
出处
《科学技术与工程》
北大核心
2025年第20期8424-8434,共11页
Science Technology and Engineering
基金
国家外国专家项目(G2022023020L)
山东省重大科技创新工程(2019JZZY020105)
甘肃省重点研发计划(22YF7FH224)。
