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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 979

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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
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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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Published Papers (1 paper)

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Research

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 289
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
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