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Advances in Hydrogeological Investigations: Field Monitoring, GIS, AI, Remote Sensing, Geophysical Techniques, and Hydrochemical Analysis

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrogeology".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2098

Special Issue Editors

Dr. Ismael Ibraheem

E-Mail Website
Guest Editor
Institute of Geophysics and Meteorology, University of Cologne, Pohligstrasse 3, 50969 Cologne, Germany
Interests: magnetic; gravity; ERT and TEM data acquisition; processing & inversion techniques of applied geophysics; environmental and groundwater geophysics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Abdelazim Negm

E-Mail Website
Guest Editor
Water and Water structures Engineering Dept, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt
Interests: sustainability studies and sustainable use and management of water resources
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The growing demand for sustainable groundwater management has driven significant advancements in hydrogeological investigations, integrating cutting-edge technologies such as Geographic Information Systems (GIS), remote sensing (RS), artificial intelligence (AI), geophysical techniques, and hydrochemical analysis. These interdisciplinary approaches have revolutionized how researchers assess, monitor, and model groundwater systems, enabling a more comprehensive understanding of aquifers, recharge zones, contamination sources, and groundwater–surface water interactions.

Remote sensing technologies, including satellite imagery, airborne sensors, and radar systems, offer large-scale and real-time data for identifying groundwater potential zones, monitoring environmental changes, and assessing recharge and discharge areas. Similarly, geophysical techniques provide essential subsurface insights by characterizing geological formations, aquifer structures, and hydrogeological parameters such as porosity and permeability. The integration of GIS enhances spatial data analysis, mapping, and visualization, providing a holistic view of groundwater resources.

Recent breakthroughs in AI and machine learning have further strengthened hydrogeological investigations by enabling predictive modeling, data-driven decision-making, and the automated interpretation of complex datasets. AI techniques, such as deep learning and neural networks, can optimize data fusion, enhance accuracy in groundwater potential mapping, and improve risk assessment models for contamination and resource depletion. Hydrochemical analysis remains a cornerstone of groundwater studies, offering critical insights into water quality, contamination trends, and the impact of anthropogenic activities on aquifers.

This Special Issue aims to bring together innovative research and state-of-the-art methodologies that leverage these advanced techniques to improve groundwater exploration, monitoring, and management. We encourage submissions that demonstrate novel applications, case studies, and cutting-edge developments in hydrogeological investigations.

Topics of interest include, but are not limited to:

  • Integration of RS, GIS, AI, and geophysical techniques in groundwater exploration;
  • Advanced hydrogeological field monitoring and data acquisition methods;
  • Groundwater modeling and predictive analytics using AI and machine learning;
  • Three-dimensional aquifer characterization and mapping;
  • Groundwater recharge assessment and aquifer sustainability analysis;
  • Delineation of contamination sources and mapping of groundwater pollution;
  • Groundwater–surface water interaction monitoring and ecosystem connectivity;
  • Transboundary groundwater management, assessment, and challenges;
  • Application of hydrochemical analysis in groundwater quality assessment;
  • Climate change impacts on groundwater resources and mitigation strategies;
  • Remote sensing-based approaches for groundwater resource management;
  • GIS-based spatial analysis for groundwater vulnerability mapping;
  • Early warning systems for groundwater depletion and pollution;
  • Hydrogeological impact of extreme weather events (floods, droughts) on groundwater systems;
  • Big data applications in hydrogeology and water resources assessment;
  • Groundwater isotopic techniques for tracing recharge sources and contamination pathways.

We invite researchers, hydrogeologists, geoscientists, and environmental professionals to contribute original research articles and comprehensive review papers that push the boundaries of hydrogeological investigations. Your contributions will help shape the future of groundwater science, fostering sustainable management strategies and innovative solutions to global water challenges.

Dr. Ismael Ibraheem
Prof. Dr. Abdelazim Negm
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • remote sensing
  • geophysical methods
  • geographical information system (GIS)
  • joint interpretation
  • hydrogeophysical investigations
  • groundwater investigation
  • groundwater monitoring
  • groundwater assessment
  • groundwater sustainability
  • aquifer characterization
  • sustainable water management
  • groundwater quality
  • groundwater contamination
  • groundwater–surface water interaction
  • groundwater resources
  • advanced groundwater modeling
  • investigation of aquifer heterogeneities
  • regional and transboundary groundwater

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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (3 papers)

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Research

28 pages, 5969 KiB  
Article
Geospatial Analysis of Chloride Hot Spots and Groundwater Vulnerability in Southern Ontario, Canada
by Ceilidh Mackie, Rachel Lackey and Jana Levison
Water 2025, 17(16), 2484; https://doi.org/10.3390/w17162484 - 21 Aug 2025
Abstract
Elevated chloride (Cl−) concentrations in surface water and groundwater are an increasing concern in cold region urban environments, largely due to long-term road salt application. This study investigates the Cl− distribution across southern Ontario, Canada, using geospatial methods to identify [...] Read more.
Elevated chloride (Cl−) concentrations in surface water and groundwater are an increasing concern in cold region urban environments, largely due to long-term road salt application. This study investigates the Cl− distribution across southern Ontario, Canada, using geospatial methods to identify contamination hot spots and assess groundwater vulnerability at both regional and watershed scales. Chloride data from 2001 to 2010 and 2011 to 2020 were compiled from public sources and interpolated using inverse distance weighting. A regional-scale vulnerability index was developed using slope (SL), surficial geology (SG), and land use (LU) (SL-SG-LU), and compared it to a more detailed DRASTIC-LU index within the Credit River watershed. Results show that Cl− hot spots are concentrated in urbanized areas, including the Greater Toronto Area and Golden Horseshoe, with some rural zones also exhibiting elevated concentrations. Vulnerability mapping corresponded well with the observed Cl− patterns and highlighted areas at risk for groundwater discharge to surface waters. While the DRASTIC-LU method offered finer resolution, the simplified SL-SG-LU index effectively captured broad vulnerability trends and is suitable for data-limited regions. This work provides a transferable framework for identifying Cl− risk areas and supports long-term monitoring and management strategies in cold climate watersheds. Full article
(This article belongs to the Special Issue Advances in Hydrogeological Investigations: Field Monitoring, GIS, AI, Remote Sensing, Geophysical Techniques, and Hydrochemical Analysis)
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30 pages, 9692 KiB  
Article
Integrating GIS, Remote Sensing, and Machine Learning to Optimize Sustainable Groundwater Recharge in Arid Mediterranean Landscapes: A Case Study from the Middle Draa Valley, Morocco
by Adil Moumane, Abdessamad Elmotawakkil, Md. Mahmudul Hasan, Nikola Kranjčić, Mouhcine Batchi, Jamal Al Karkouri, Bojan Đurin, Ehab Gomaa, Khaled A. El-Nagdy and Youssef M. Youssef
Water 2025, 17(15), 2336; https://doi.org/10.3390/w17152336 - 6 Aug 2025
Viewed by 695
Abstract
Groundwater plays a crucial role in sustaining agriculture and livelihoods in the arid Middle Draa Valley (MDV) of southeastern Morocco. However, increasing groundwater extraction, declining rainfall, and the absence of effective floodwater harvesting systems have led to severe aquifer depletion. This study applies [...] Read more.
Groundwater plays a crucial role in sustaining agriculture and livelihoods in the arid Middle Draa Valley (MDV) of southeastern Morocco. However, increasing groundwater extraction, declining rainfall, and the absence of effective floodwater harvesting systems have led to severe aquifer depletion. This study applies and compares six machine learning (ML) algorithms—decision trees (CART), ensemble methods (random forest, LightGBM, XGBoost), distance-based learning (k-nearest neighbors), and support vector machines—integrating GIS, satellite data, and field observations to delineate zones suitable for groundwater recharge. The results indicate that ensemble tree-based methods yielded the highest predictive accuracy, with LightGBM outperforming the others by achieving an overall accuracy of 0.90. Random forest and XGBoost also demonstrated strong performance, effectively identifying priority areas for artificial recharge, particularly near ephemeral streams. A feature importance analysis revealed that soil permeability, elevation, and stream proximity were the most influential variables in recharge zone delineation. The generated maps provide valuable support for irrigation planning, aquifer conservation, and floodwater management. Overall, the proposed machine learning–geospatial framework offers a robust and transferable approach for mapping groundwater recharge zones (GWRZ) in arid and semi-arid regions, contributing to the achievement of Sustainable Development Goals (SDGs))—notably SDG 6 (Clean Water and Sanitation), by enhancing water-use efficiency and groundwater recharge (Target 6.4), and SDG 13 (Climate Action), by supporting climate-resilient aquifer management. Full article
(This article belongs to the Special Issue Advances in Hydrogeological Investigations: Field Monitoring, GIS, AI, Remote Sensing, Geophysical Techniques, and Hydrochemical Analysis)
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17 pages, 2988 KiB  
Article
Comparative Analysis of Nonlinear Models from Different Domains: A Case Study on the Quality of Groundwater in an Alluvial Aquifer in Northwestern Croatia
by Ivan Kovač, Marko Šrajbek, Nikola Sakač and Jasna Nemčić-Jurec
Water 2025, 17(9), 1378; https://doi.org/10.3390/w17091378 - 2 May 2025
Viewed by 526
Abstract
In groundwater quality analysis, nonlinear models are typically used, with domains spanning the entire real number line. In this study, alongside these models (Logistic, Gompertz and Richards), nonlinear models defined based on functions whose domain is only the positive part of the real [...] Read more.
In groundwater quality analysis, nonlinear models are typically used, with domains spanning the entire real number line. In this study, alongside these models (Logistic, Gompertz and Richards), nonlinear models defined based on functions whose domain is only the positive part of the real number line are presented (Michaelis–Menten, Hill 1 and 2 and Rosin–Rammler 1 and 2). Two case studies were observed in the paper: (i) the dependence of nitrate concentration on the pumping rate in the Bartolovec wellfield, and (ii) the dependence of nitrate concentration on the distance from the source of pollution in the Varaždin wellfield. Both wellfields are located in the alluvial aquifer in northwestern Croatia. In this way, the curves obtained on the basis of the mentioned mathematical functions were fitted to the experimental data. The results show a good fit, so that the values of the coefficients of determination R2 are greater than 0.82 for the case study (i) and greater than 0.96 for the case study (ii). Since the models differ in the number of parameters (e.g., three parameters for Michaelis–Menten and five parameters for Rosin–Rammler), the corrected Akaike information criterion (AICc) was used for their comparison. In this way, the best fit for the case study (i) was obtained for the Rosin–Rammler 1 model, while for the case study (ii), it was for the Hill 1 model. A t-test was performed for all models, and they can be considered reliable at a significance level of 0.05. However, t-values and p-values were also calculated for each parameter of each model. Based on these results, it is concluded that all model parameters can be considered reliable at a significance level of 0.05 only for the Hill 1 and Rosin–Rammler 1 models in both case studies. For this reason, these models can generally be considered the best fit to the experimental data. The study demonstrates the superiority of nonlinear models with domains restricted to positive real numbers (e.g., Hill 1, Rosin–Rammler 1) over traditional models (e.g., Logistic, Richards) in groundwater quality analysis. These findings offer practical tools for predicting contaminant extremes (e.g., maximum/minimum concentrations) and optimizing groundwater management strategies. Full article
(This article belongs to the Special Issue Advances in Hydrogeological Investigations: Field Monitoring, GIS, AI, Remote Sensing, Geophysical Techniques, and Hydrochemical Analysis)
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