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基于 KELM 的趵突泉泉域地下水流替代模型

Alternative model of groundwater flow in Baotu Spring area based on KELM

期刊信息

合肥工业大学(自然科学版),2025年1月,第48卷第1期:85-91

DOI: 10.3969/j.issn.1003-5060.2025.01.013

作者信息

王子健 $ ^{1} $,骆乾坤 $ ^{1} $,李迎春 $ ^{2} $,刘鑫 $ ^{1} $,邓亚平 $ ^{1} $,钱家忠 $ ^{1} $

(1. 合肥工业大学资源与环境工程学院,安徽合肥 230009;2. 安徽省公益性地质调查管理中心,安徽合肥 230092)

摘要和关键词

摘要: 文章以济南市趵突泉泉域为研究区, 采用核极限学习机(kernel extreme learning machine, KELM)建立泉域地下水流数值模型的替代模型, 使用拉丁超立方抽样(Latin hypercube sampling, LHS)方法确定60组地下水开采方案用于训练KELM模型, 通过对比地下水流数值模型的模拟结果与替代模型输出的结果, 评价所建立替代模型的性能。结果表明: 替代模型输出的地下水位值与地下水流数值模型模拟得到的地下水位值基本接近, 且模型的运行时间减少了约99.62%。说明该模型可作为趵突泉泉域地下水流数值模型的替代模型, 可提高区域地下水优化管理模型的求解效率。

关键词: 地下水数值模拟;趵突泉泉域;替代模型;核极限学习机(KELM);拉丁超立方抽样(LHS)

Authors

WANG Zijian $ ^{1} $, LUO Qiankun $ ^{1} $, LI Yingchun $ ^{2} $, LIU Xin $ ^{1} $, DENG Yaping $ ^{1} $, QIAN Jiazhong $ ^{1} $

(1. School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, China; 2. Public Geological Survey Management Center of Anhui Province, Hefei 230092, China)

Abstract and Keywords

Abstract: In this paper, the Baotu Spring area in Jinan City is used as the study region to establish a regional groundwater flow numerical simulation model. Based on this, the kernel extreme learning machine (KELM) is used to develop an alternative model to the numerical model of groundwater flow in the spring area. The Latin hypercube sampling (LHS) method was used to obtain 60 sets of groundwater extraction scenarios for training the KELM model. The performance of the developed alternative model was evaluated by comparing the simulation results of the numerical model with the output of the alternative model. The results show that the groundwater levels output by the alternative model are basically close to those obtained from the simulation of the numerical model, and the running time of the alternative model is reduced by about 99.62%, indicating that the KELM model can be used as an alternative model to the numerical model of the groundwater flow in the Baotu Spring area to improve the efficiency of solving the regional groundwater optimal management model.

Keywords: groundwater numerical simulation; Baotu Spring area; alternative model; kernel extreme learning machine(KELM); Latin hypercube sampling(LHS)

基金信息

国家重点研发计划资助项目(2022YFC3702204);安徽省自然科学基金资助项目(JZ2022AKZR0451)

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