Accelerating regional-scale groundwater flow simulations with a hybrid deep neural network model incorporating mixed input types: A case study of the northeast Qatar aquifer

IF 2.2 3区 工程技术 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Ali Al-Maktoumi, Mohammad Mahdi Rajabi, Slim Zekri, Rajesh Govindan, Aref Panjehfouladgaran, Zahra Hajibagheri
{"title":"Accelerating regional-scale groundwater flow simulations with a hybrid deep neural network model incorporating mixed input types: A case study of the northeast Qatar aquifer","authors":"Ali Al-Maktoumi, Mohammad Mahdi Rajabi, Slim Zekri, Rajesh Govindan, Aref Panjehfouladgaran, Zahra Hajibagheri","doi":"10.2166/hydro.2024.275","DOIUrl":null,"url":null,"abstract":"<p>This study presents the ‘Dual Path CNN-MLP’, a novel hybrid deep neural network (DNN) architecture that merges the strengths of convolutional neural networks (CNNs) and multilayer perceptrons (MLPs) for regional groundwater flow simulations. This model stands out from previous DNN approaches by managing mixed input types, including both imagery and numerical vectors. Such flexibility allows the diverse nature of groundwater data to be efficiently utilized without the need to convert it into a uniform format, which often leads to oversimplification or unnecessary expansion of the dataset. When applied to the northeast Qatar aquifer, the model demonstrates high accuracy in simulating transient groundwater flow fields, benchmarked against the well-established MODFLOW model. The model's efficacy is confirmed through <em>k</em>-fold cross-validation, showing an error margin of less than 12% across all examined locations. The study also examines the model's ability to perform uncertainty analysis using Monte Carlo simulations, finding that it achieves around 1% average absolute percentage error in estimating the mean hydraulic head. Errors are mostly found in areas with significant variations in the hydraulic head. Switching to this machine learning model from the conventional MODFLOW simulator boosts computational efficiency by about 99%, showcasing its advantage for tasks like uncertainty analysis in repetitive groundwater simulations.</p>","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Hydroinformatics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.2166/hydro.2024.275","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

This study presents the ‘Dual Path CNN-MLP’, a novel hybrid deep neural network (DNN) architecture that merges the strengths of convolutional neural networks (CNNs) and multilayer perceptrons (MLPs) for regional groundwater flow simulations. This model stands out from previous DNN approaches by managing mixed input types, including both imagery and numerical vectors. Such flexibility allows the diverse nature of groundwater data to be efficiently utilized without the need to convert it into a uniform format, which often leads to oversimplification or unnecessary expansion of the dataset. When applied to the northeast Qatar aquifer, the model demonstrates high accuracy in simulating transient groundwater flow fields, benchmarked against the well-established MODFLOW model. The model's efficacy is confirmed through k-fold cross-validation, showing an error margin of less than 12% across all examined locations. The study also examines the model's ability to perform uncertainty analysis using Monte Carlo simulations, finding that it achieves around 1% average absolute percentage error in estimating the mean hydraulic head. Errors are mostly found in areas with significant variations in the hydraulic head. Switching to this machine learning model from the conventional MODFLOW simulator boosts computational efficiency by about 99%, showcasing its advantage for tasks like uncertainty analysis in repetitive groundwater simulations.

利用混合输入类型的混合深度神经网络模型加速区域尺度地下水流模拟:卡塔尔东北部含水层案例研究
本研究介绍了 "双路径 CNN-MLP",这是一种新型混合深度神经网络(DNN)架构,它融合了卷积神经网络(CNN)和多层感知器(MLP)的优势,可用于区域地下水流模拟。与以往的 DNN 方法相比,该模型可管理混合输入类型,包括图像和数字向量。这种灵活性使地下水数据的多样性得到有效利用,而无需将其转换为统一格式,因为统一格式往往会导致数据集的过度简化或不必要的扩展。在应用于卡塔尔东北部含水层时,该模型以成熟的 MODFLOW 模型为基准,在模拟瞬态地下水流场方面表现出很高的准确性。该模型的有效性通过 k 倍交叉验证得到了证实,在所有考察地点的误差率均小于 12%。研究还利用蒙特卡罗模拟对模型进行了不确定性分析,发现该模型在估算平均水头时平均绝对误差约为 1%。误差主要出现在水头变化较大的区域。从传统的 MODFLOW 模拟器切换到该机器学习模型后,计算效率提高了约 99%,显示了它在重复性地下水模拟中进行不确定性分析等任务时的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Journal of Hydroinformatics
Journal of Hydroinformatics 工程技术-工程:土木
CiteScore
4.80
自引率
3.70%
发文量
59
审稿时长
3 months
期刊介绍: Journal of Hydroinformatics is a peer-reviewed journal devoted to the application of information technology in the widest sense to problems of the aquatic environment. It promotes Hydroinformatics as a cross-disciplinary field of study, combining technological, human-sociological and more general environmental interests, including an ethical perspective.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信