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 [* Cross-Reference *]

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Ali Al‐Maktoumi; Mohammad Mahdi Rajabi; Slim Zekri; Rajesh Govindan; Aref Panjehfouladgaran; Zahra Hajibagheri, 2026, "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 [* Cross-Reference *]", https://lore.list.lu/citation?persistentId=perma:LIST.KJIK6R, LIST Open Repository, V1

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Description	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. (2024-05-28) *This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.2166/hydro.2024.275 for the original and latest version of the publication* (2026-07-01)
Subject	Earth and Environmental Sciences; Engineering; Physics
Keyword	Aquifer, Groundwater flow, Groundwater, Scale (ratio), Groundwater model, Hydrology (agriculture), Environmental science, Artificial neural network, Geology, Flow (mathematics), Geotechnical engineering, Geography, Computer science, Cartography, Mathematics, Machine learning
License/Data Use Agreement	CC BY 4.0


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Persistent Identifier	perma:LIST.KJIK6R
Publication Date	2026-07-06
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 [* Cross-Reference *]
Other Identifier	https://doi.org/10.2166/hydro.2024.275 OpenAlex ID: https://openalex.org/W4399130561
Author	Ali Al‐Maktoumi (Sultan Qaboos University) - ORCID: https://orcid.org/0000-0001-8766-5993 Mohammad Mahdi Rajabi (Tarbiat Modares University, University of Luxembourg, Luxembourg Institute of Science and Technology) - ORCID: https://orcid.org/0000-0002-2181-6637 Slim Zekri (Sultan Qaboos University) Rajesh Govindan (Hamad bin Khalifa University) Aref Panjehfouladgaran (Western University, Tarbiat Modares University) Zahra Hajibagheri (Tarbiat Modares University)
Point of Contact	Use email button above to contact. LIST QDKM (LIST)
Description	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. (2024-05-28) *This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.2166/hydro.2024.275 for the original and latest version of the publication* (2026-07-01)
Subject	Earth and Environmental Sciences; Engineering; Physics
Keyword	Aquifer Groundwater flow Groundwater Scale (ratio) Groundwater model Hydrology (agriculture) Environmental science Artificial neural network Geology Flow (mathematics) Geotechnical engineering Geography Computer science Cartography Mathematics Machine learning
Topic Classification	Reservoir Engineering and Simulation Methods Groundwater flow and contamination studies Hydrological Forecasting Using AI
Deposit Date	2024-05-28
Data Type	Article
Data Source	Journal of Hydroinformatics

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