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New Tools and Methods for Groundwater Vulnerability Assessment

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

Deadline for manuscript submissions: 30 October 2026 | Viewed by 1203

Special Issue Editors


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Guest Editor
Department of Water and Global Change, Spanish Geological Survey (IGME), CSIC, Ríos Rosas, 23, 28003 Madrid, Spain
Interests: seawater intrusion; groundwater vulnerability; vulnerability validation; groundwater age; natural background level; climate change impacts on groundwater; nature-based solutions

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Guest Editor
Water and Mining Environment Solutions, Geological Survey of Finland, FI-02151 Espoo, Finland
Interests: groundwater; groundwater model; geochemisty; groundwater vulnerability assessment; land use and environment; climate change impacts on groundwater

Special Issue Information

Dear Colleagues,

Groundwater is the main source of freshwater in many arid and semi-arid regions worldwide. Ensuring both the qualitative and quantitative protection of groundwater resources is critical for maintaining long-term sustainability and water security. The level of protection required is not only by the intrinsic vulnerability of aquifers but also by the risk of degradation due to anthropogenic activities such as agricultural practices, industrial operations, urban development, groundwater abstraction, and natural hazards, including floods or droughts.

For several decades, index-based methods have been widely used to assess groundwater vulnerability to surface-derived pollution. In recent years, there has been an increasing focus on integrating machine learning techniques into groundwater vulnerability assessments. Additionally, emerging frameworks now consider groundwater vulnerability in the context of pumping, droughts, and floods, offering innovative perspectives for groundwater protection.

This Special Issue seeks to highlight recent scientific advancements in this field. We invite contributions that address all aspects of groundwater contamination, aquifer characterization, validation methodologies, the impact of climate change on groundwater vulnerability, and novel approaches for assessing groundwater vulnerability to pollution and droughts. The specific topics of interest include, but are not limited to, the following:

  • Groundwater vulnerability to pollution;
  • Groundwater vulnerability to droughts and floods;
  • Groundwater vulnerability in coastal aquifers;
  • Aquifer characterization for intrinsic vulnerability assessment;
  • Validation methods of groundwater vulnerability (e.g., natural background levels, denitrification, and others);
  • Risk mitigation strategies;
  • Impact of climate change on groundwater vulnerability;
  • Pollution prevention measures;
  • Remediation technologies;
  • Assessment techniques for groundwater vulnerability, including artificial intelligence, GIS-based models, index-based methods, and statistical methods.

Dr. Leticia Baena-Ruiz
Dr. Samrit Luoma
Guest Editors

Manuscript Submission Information

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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

  • groundwater contamination
  • groundwater vulnerability
  • climate change
  • groundwater vulnerability validation
  • artificial intelligence
  • GIS models
  • aquifer characterization
  • risk mitigation
  • pollution prevention

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Published Papers (1 paper)

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Research

16 pages, 16252 KB  
Article
Optimized Groundwater Vulnerability Assessment Using Machine Learning: A Case Study of Luyi County, China
by Chengdong Liu, Mingming Wang, Huiyun Tian, Jiyi Jiang, Yi Wen, Xiaojing Zhao and Qi Zhang
Water 2026, 18(5), 624; https://doi.org/10.3390/w18050624 - 5 Mar 2026
Viewed by 467
Abstract
Groundwater vulnerability assessment is crucial for sustainable water resources management and pollution prevention. Taking Luyi County, Henan Province, China, as the study area, this study applies three supervised machine learning algorithms—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)—to establish [...] Read more.
Groundwater vulnerability assessment is crucial for sustainable water resources management and pollution prevention. Taking Luyi County, Henan Province, China, as the study area, this study applies three supervised machine learning algorithms—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)—to establish classification models using nitrate nitrogen (NO3–N) concentrations above 10 mg/L as the target variable. The predicted probability of contamination is adopted as an indicator of groundwater vulnerability. Model performance was comprehensively assessed using multiple evaluation metrics. The results show that all three models exhibited stable and strong predictive performance, with Area Under the Curve (AUC) values ranging from 0.91 to 0.94 and accuracy exceeding 86.5%. Pearson and Spearman correlation analyses were performed between observed NO3–N concentrations from 77 monitoring wells and the groundwater vulnerability results, indicating overall better performance than the traditional index-overlay method. Feature importance analysis based on the RF and XGBoost models suggests that aquifer hydraulic conductivity is the most critical controlling factor, followed by aquifer thickness and recharge, whereas land use and the remaining indicators exhibit comparatively lower contributions. The resulting vulnerability maps indicate that areas with high groundwater vulnerability are mainly concentrated in the western and southeastern parts of the study area, where agricultural activities are relatively intensive. Full article
(This article belongs to the Special Issue New Tools and Methods for Groundwater Vulnerability Assessment)
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