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Water Quality Analytics in the Digital Era: Methods, Models and...
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Water Quality Analytics in the Digital Era: Methods, Models and Management

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

Deadline for manuscript submissions: closed (20 February 2026) | Viewed by 921

Special Issue Editors

Dr. Irina Fierascu

E-Mail Website
Guest Editor
National Institute for Research & Development in Chemistry and Petrochemistry—ICECHIM Bucharest, 202 Spl. Independentei, 060021 Bucharest, Romania
Interests: water treatment; nanomaterials; micropollutants; pollutants removal; advanced materials
Special Issues, Collections and Topics in MDPI journals
Dr. Rodica Mihaela Frîncu

E-Mail Website
Guest Editor Assistant
National Institute for Research and Development in Chemistry and Petrochemistry—ICECHIM 202 Splaiul Independentei, Bucharest, Romania
Interests: water quality; data analysis; nutrients recycling; water treatment; artificial intelligence

Special Issue Information

Dear Colleagues,

Water quality data analysis serves multiple essential roles across public health, environmental management, regulatory compliance, and industry operations. Analyzing water quality data allows us to assess whether water is safe for human consumption, recreation, and aquatic life. Utilities, industries, and municipalities use data analysis to demonstrate compliance with local, national, and international standards for drinking water, wastewater discharge, and environmental protection. Water quality analysis informs decisions on water treatment, resource allocation, conservation, and infrastructure investment.

By evaluating long-term trends and detecting anomalies, water managers can identify changes in water quality over time and space, guiding targeted responses before issues escalate. Integrated analysis of water data supports scientific studies, the modeling of pollution sources and impacts, and the development of evidence-based policies for water resource management.

In this Special Issue, we welcome original research articles, as well as comprehensive reviews. Topics may include, but are not limited to, the following:

  • Statistical and geospatial analysis of water quality datasets;
  • Machine learning and artificial intelligence in water quality monitoring;
  • Time series analysis and early warning systems;
  • Integration of satellite and in situ water quality data;
  • Emerging contaminants and pollution dynamics;
  • Data-driven modeling for source identification and water quality management;
  • Uncertainty quantification and risk assessment based on water quality data;
  • Big data analytics, fusion, and visualization for decision making;
  • Challenges and New Developments in Data Integration for Water Quality Management.

We hope this Special Issue will provide a platform for exchanging knowledge, showcasing new, innovative practices, and fostering collaboration across the scientific community working in this field.

We look forward to receiving your contributions!

Dr. Irina Fierascu
Guest Editor

Dr. Rodica Mihaela Frîncu
Guest Editor Assistant

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 250 words) can be sent to the Editorial Office for assessment.

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

  • water quality
  • pollutants
  • micropollutans
  • trend analysis
  • data analysis
  • machine learning
  • big data
  • water management

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

Published Papers (1 paper)

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Research

25 pages, 12554 KB  
Article
An Explainable Artificial Intelligence-Driven Framework for Predicting Groundwater Irrigation Suitability in Hard-Rock Aquifers: Moving Beyond Traditional Bivariate Diagnostics
by Mohamed Hussein Yousif, Quanrong Wang, Anurag Tewari, Abara A. Biabak Indrick, Hafizou M. Sow, Yousif Hassan Mohamed Salh and Wakeel Hussain
Water 2026, 18(7), 854; https://doi.org/10.3390/w18070854 - 2 Apr 2026
Viewed by 603
Abstract
Groundwater is the primary source of irrigation in many semi-arid hard-rock aquifer regions. Yet, its suitability assessment is often hindered by the nonlinear hydrochemical dynamics that traditional bivariate tools, such as the U.S. Salinity Laboratory (USSL) diagram, cannot adequately resolve. To overcome this [...] Read more.
Groundwater is the primary source of irrigation in many semi-arid hard-rock aquifer regions. Yet, its suitability assessment is often hindered by the nonlinear hydrochemical dynamics that traditional bivariate tools, such as the U.S. Salinity Laboratory (USSL) diagram, cannot adequately resolve. To overcome this limitation, we developed an explainable artificial intelligence (XAI) framework that predicts irrigation suitability categories directly from hydrochemical variables, without relying on calculated indices. Using 1872 post-monsoon groundwater samples from Telangana, India, we trained three ensemble tree-based classifiers (Random Forest, LightGBM, and XGBoost) on 11 hydrochemical variables (Na+, K+, Ca2+, Mg2+, HCO3−, Cl−, F−, NO3−, SO42−, pH, and total hardness). Class imbalance was addressed using the Synthetic Minority Over-sampling Technique (SMOTE), and model hyperparameters were optimized with Optuna. Among the tested models, LightGBM achieved the best performance (balanced accuracy = 0.938). Model interpretability was enabled using Shapley Additive Explanations (SHAP), supported by Piper and Gibbs diagrams, revealing a critical distinction between sodicity-driven salinity and hardness-driven mineralization, identifying calcium-saturated waters for which gypsum amendment can be chemically futile. To bridge the gap between algorithmic accuracy and operational simplicity, we distilled SHAP explanations into linear heuristics and quantified the trade-off between accuracy and simplicity. Accordingly, we proposed a tiered hydrochemical triage framework in which quantitative heuristics handled approximately 62.5% of the routine samples, while XAI resolved the complex and ambiguous cases. Overall, the proposed framework transforms classic suitability assessment tools into an adaptable, evidence-informed, proactive decision-support system for sustainable agricultural water management under increasing environmental stress. Full article
(This article belongs to the Special Issue Water Quality Analytics in the Digital Era: Methods, Models and Management)
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