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Application of Artificial Intelligence (AI) in Water Quality Monitoring, 2nd Edition

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: 20 October 2026 | Viewed by 2557

Editor

Prof. Dr. Tianhong Li

E-Mail Website
Guest Editor
College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
Interests: river ecology; LUCC; water resource; non-point pollution; remote sensing; GIS; environmental modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Water quality monitoring is a key step in ensuring the sustainable utilization, security, and aquatic ecological environment of water resources. By monitoring water quality, pollutants, bacteria, and other harmful substances in water bodies can be detected and identified early on, and corresponding measures can be taken to protect public health and ecosystems. Water quality monitoring also helps in assessing the sustainability of water resources and guiding rational water resource management and decision making. The rapidly developing artificial intelligence technology of recent years possesses real-time monitoring capabilities, big data analysis and pattern recognition capabilities, intelligent decision making capabilities, and data integration and joint analysis capabilities. These qualities can overcome some of the challenges faced by traditional water quality monitoring methods, make up for the limitations of traditional methods, and have great application prospects in water quality monitoring.

This Special Issue is interdisciplinary and encourages methodological pluralism. We welcome research-based manuscript submissions from scholars and practitioners working in water quality monitoring, information sciences, environmental sciences, ecology, and water policy studies.

Prof. Dr. Tianhong Li
Guest Editor

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-anonymized 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 monitoring
  • artificial intelligence
  • deep learning
  • machine learning
  • precise regulation
  • intelligent recognition
  • predictive warning
  • real-time monitoring
  • model optimization

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Related Special Issue

Published Papers (3 papers)

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Research

21 pages, 17042 KB  
Article
A Machine Learning Approach for Water Quality Assessment in the Lower Rio Grande Valley Watershed
by Saika Nowshin Nowrin, Chu-Lin Cheng, Jungseok Ho, Jinwoo An and Fatemeh Nazari
Water 2026, 18(15), 1812; https://doi.org/10.3390/w18151812 - 26 Jul 2026
Viewed by 378
Abstract
Water quality analysis plays an essential role in maintaining the health and sustainability of river ecosystems, especially in semi-arid regions like the Arroyo Colorado Watershed in South Texas. Since the river is a vital source of water supply for local communities, agriculture, and [...] Read more.
Water quality analysis plays an essential role in maintaining the health and sustainability of river ecosystems, especially in semi-arid regions like the Arroyo Colorado Watershed in South Texas. Since the river is a vital source of water supply for local communities, agriculture, and wildlife, it faces significant challenges and pollution from land use changes, climate variation, and agricultural runoff. Continuous monitoring and assessment of water quality parameters and their temporal variability are essential to ensure the drinking water supply and aquatic ecosystem health. However, comprehensive laboratory-based water quality investigations are often constrained by higher costs, logistical complexity, and limited manpower. As a result, monitoring datasets are often not available for all water quality parameters, or the datasets may be incomplete. To address such challenges, the objective of this study was to evaluate the potential of water quality index (WQI)-based assessment supported by machine learning algorithms as an alternative decision-support tool for water quality evaluation. The analysis compared four monitoring stations in the Austin and Arroyo Colorado Watersheds, with particular emphasis on one gauging station at Port Harlingen. Datasets were collected from the Texas Commission of Environmental Quality (TCEQ). A complete exploratory data analysis (EDA) was performed to understand the TCEQ water quality datasets containing sixteen parameters, and seven water quality parameters were selected based on multicollinearity checks. It was observed that seven independent water quality parameters (dissolved oxygen, ammonia, nitrate, phosphorus, temperature, fecal coliform, and residual non-filterable material concentrations) were identified as sufficient to define the WQI of the Austin monitoring stations. Moreover, U.S. Environmental Protection Agency (EPA)-based guidelines were utilized to scale individual parameters to a range of 0–100 to remove their magnitude and correlation-based bias. These parameters were further analyzed using machine learning techniques, i.e., principal component analysis, K-means, and one-class support vector machine, to compute the relative importance based on their fluctuation within the temporal dataset. Finally, the mean WQI model was developed for Port Harlingen and achieved a strong agreement with the National Sanitation Foundation (NSF) WQI (R2 = 0.91). These findings demonstrate the applicability of the proposed data-driven WQI framework for regional water quality assessment and comparative analysis across watersheds. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence (AI) in Water Quality Monitoring, 2nd Edition)
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34 pages, 9132 KB  
Article
Integrated Study on Comprehensive Water Quality Assessment and Short-Term Early Warning for Multi-Section Rivers: Comparison of WQI-TOPSIS-Entropy Weight Indices, Anomaly Identification, and One-Step Prediction via Machine Learning (2019–2025)
by Niegui Li, Wei Zhang, Xinxin Jiang, Haolin Liu and Xiujun Liu
Water 2026, 18(12), 1450; https://doi.org/10.3390/w18121450 - 12 Jun 2026
Viewed by 481
Abstract
To support refined water quality evaluation and short-term early warning in multi-section river systems, this study developed three percentile-based composite indices: the Water Quality Index (WQI), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the Entropy Weight Method (EWM). [...] Read more.
To support refined water quality evaluation and short-term early warning in multi-section river systems, this study developed three percentile-based composite indices: the Water Quality Index (WQI), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the Entropy Weight Method (EWM). Monthly multi-parameter monitoring data from 2019 to 2025 were used, covering ten river sections (P1–P5, M1–M5). The three indices were compared in terms of statistical distribution, methodological consistency, and anomaly response. An integrated assessment–prediction framework was further established. Within this framework, a one-step prediction scheme was applied to evaluate four models: Long Short-Term Memory networks (LSTM), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost). The results show that WQI scores were generally high and fluctuated within a narrow range. A clear “ceiling effect” was observed in the moderate-to-high grade intervals. WQI also showed weak consistency with TOPSIS and EWM (r ≈ 0.29–0.32). In contrast, TOPSIS and EWM were more sensitive to water quality fluctuations and extreme risks, and were moderately correlated with each other (r ≈ 0.53). Using TOPSIS < 50 as the threshold, 49 severe anomalous events were identified. These events were mainly clustered in February–April 2020, April–July 2023, and June–September 2025, with sections P4, M1, and M2 acting as high-incidence sites. In several typical events, WQI values remained high, indicating that reliance on WQI alone may delay early warning. Prediction results further reveal that the choice of index strongly affects sequence predictability. Taking XGBoost as the reference, the median validation R2 followed a stable gradient: WQI (0.807) > TOPSIS (0.723) > EWM (0.594). XGBoost yielded positive R2 values across all indices and sections. It also achieved the most robust overall performance and the strongest cross-site, cross-index generalization capability. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence (AI) in Water Quality Monitoring, 2nd Edition)
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24 pages, 6146 KB  
Article
Feasibility of Conditional Tabular Generative Adversarial Networks for Ecologically Plausible Synthetic River Water-Quality Data: A Statistical and Ecological Similarity Assessment
by Orhan Ibram, Luminita Moraru, Simona Moldovanu, Catalina Maria Topa, Catalina Iticescu and Puiu-Lucian Georgescu
Water 2026, 18(2), 214; https://doi.org/10.3390/w18020214 - 14 Jan 2026
Viewed by 1097
Abstract
Reliable biological datasets, especially those integrating biotic indices such as the Saprobic Index, are scarce, limiting machine and deep learning applications in aquatic ecosystem assessments. This study evaluates Conditional Tabular Generative Adversarial Networks (CTGANs) for generating synthetic datasets that combine physico-chemical parameters with [...] Read more.
Reliable biological datasets, especially those integrating biotic indices such as the Saprobic Index, are scarce, limiting machine and deep learning applications in aquatic ecosystem assessments. This study evaluates Conditional Tabular Generative Adversarial Networks (CTGANs) for generating synthetic datasets that combine physico-chemical parameters with a biological index (Saprobic Index) from multiple monitoring stations in the lower Danube River. Beyond univariate distributional agreement, we assess whether ecologically meaningful multivariate relationships are preserved in the synthetic tables. To support this, we propose an ecology-oriented validation workflow that combines distributional tests with correlation structure and clustering diagnostics across stations. Real monitoring datasets were statistically modelled and recreated using CTGANs, then qualitatively assessed for realism. Comparisons between synthetic and real data employed box plots, Wilcoxon rank-sum tests, correlation matrices, and K-means clustering across stations. Stable variables, including pH, total dissolved solids, and chemical oxygen demand, were well replicated, showing no significant distributional differences (p > 0.05). Conversely, dynamic parameters such as dissolved oxygen, total nitrogen, and suspended solids exhibited notable discrepancies (p < 0.05). Correlation analyses indicated that several strong associations present in the observed data (e.g., total nitrogen–nitrate and total nitrogen–electrical conductivity) were substantially weaker in the synthetic dataset. Overall, a CTGAN can reproduce several marginal patterns but may fail to preserve key ecological linkages, which constrains its use in ecological relationship-dependent inference. While promising for exploratory modelling and general trend analysis, synthetic data should be applied cautiously for studies involving seasonally influenced, biologically significant parameters. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence (AI) in Water Quality Monitoring, 2nd Edition)
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