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Water Quality Monitoring and Prediction Using New Sensors, Machine Learning and Big Data

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: closed (25 October 2025) | Viewed by 6062

Special Issue Editor

Dr. Yong Jie Wong

E-Mail Website
Guest Editor
Department of Bioenvironmental Design, Faculty of Bioenvironmental Sciences, Kyoto University of Advanced Science, Kyoto 606-8501, Japan
Interests: machine learning; statistical analysis; geographic information science; climate change; hydrological modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Access to clean water is essential for human life, but water resources around the world are under increasing pressure from population growth, climate change, and pollution. Water quality monitoring and prediction are critical for ensuring access to safe and clean water for human consumption, agriculture, and industrial use. The use of advanced technologies in water quality monitoring and prediction has the potential to improve the accuracy and efficiency of water resource management, enabling proactive responses to environmental challenges.

Advanced sensors and machine learning are key technologies that can revolutionize water quality monitoring and prediction. Low-cost, portable sensors can provide real-time data on water quality, allowing for timely responses to pollution incidents. Meanwhile, machine learning algorithms can analyze large datasets and identify patterns that may be difficult for humans to detect, predicting changes in water quality and informing management decisions. In combination with advanced technologies such as big data analytics, remote sensing, and the Internet of Things (IoT), water quality monitoring and prediction can be significantly improved. These technologies allow for monitoring over large areas, real-time data transmission, and the control of water treatment processes, ensuring sustainable water resource management.

In summary, this Special Issue aims to explore the latest advances in water quality monitoring and prediction technologies and their applications. It also aims to highlight the challenges and opportunities associated with these technologies and their potential impact on water resource management. The topics of interest include, but are not limited to, the following:

  • Advances in sensor technology for water quality monitoring;
  • Machine learning algorithms for predicting changes in water quality;
  • Applications of big data analytics in water quality management;
  • Remote sensing for monitoring water quality over large areas;
  • Uses of the Internet of Things (IoT) in real-time water quality monitoring and control;
  • Environmental monitoring and water resource management for sustainable development;
  • Applications of advanced technologies in water quality management;
  • Challenges and future directions in the development and implementation of advanced technologies for water quality monitoring and prediction.

This Special Issue will feature articles discussing the latest developments and applications regarding advanced technologies in water quality monitoring and prediction. These articles should cover topics such as the design and implementation of advanced sensors, machine learning algorithms, big data analytics, remote sensing, and the Internet of Things (IoT) in sustainable water resource management. This Special Issue aims to provide a platform for researchers, engineers, and practitioners to share their knowledge and experiences in these fields and to promote the development of new and innovative technologies for water quality monitoring and prediction. This Special Issue will contribute to advancements in knowledge in this critical area and support efforts towards preserving our water resources for future generations.

Dr. Yong Jie Wong
Guest Editor

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 monitoring
  • prediction technologies
  • advanced sensors
  • machine learning
  • big data analytics
  • remote sensing
  • Internet of Things (IoT)
  • environmental monitoring
  • water resource management

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

Published Papers (5 papers)

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Research

10 pages, 3460 KB  
Communication
Water Circulation Beneath a Hovering sUAS for Water Quality Monitoring Applications
by Erin E. Hackett, Boone Fleenor, Jensine C. Coggin, Duvall Dickerson-Evans, Nikolaos Vitzilaios, Whitney E. Schuler, Paige K. Williams and Michael L. Myrick
Water 2025, 17(24), 3481; https://doi.org/10.3390/w17243481 - 9 Dec 2025
Viewed by 38
Abstract
Water quality has traditionally been measured via in situ sensors and satellites. The latter has limited applicability for smaller inland water bodies, while the former requires significant logistics, labor, and expense for routine sampling, and reactive/spurious sampling is often not feasible as a [...] Read more.
Water quality has traditionally been measured via in situ sensors and satellites. The latter has limited applicability for smaller inland water bodies, while the former requires significant logistics, labor, and expense for routine sampling, and reactive/spurious sampling is often not feasible as a result (e.g., sampling pre-/post-storm). Consequently, small uncrewed aircraft system-based (sUAS-based) sampling has emerged as a potential solution to bridge these sampling gaps and challenges. But sampling from an sUAS is complicated by the need to pump water from depth, rather than suspending a sensor from the sUAS, due to concern over sampling sUAS-impacted waters. Here, we measure the water flow below a hovering sUAS in a laboratory by applying the particle image velocimetry flow measurement technique. Observations suggest the development of two counter-rotating vortices under the sUAS, where, in the center of the vortex pair, water is upwelled to the surface, which would, therefore, be a sampling location relatively free of contamination by the sUAS. This location coincides with the still spot on the water surface underneath the sUAS; thus, if one wanted to sample water by suspending a sensor underneath an sUAS, then the optimal sampling location would be within this still spot. Full article
(This article belongs to the Special Issue Water Quality Monitoring and Prediction Using New Sensors, Machine Learning and Big Data)
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17 pages, 3868 KB  
Article
Prolonged Summer Daytime Dissolved Oxygen Recovery in a Eutrophic Lake: High-Frequency Monitoring Diel Evidence from Taihu Lake, China
by Dong Xie, Xiaojie Chen, Yi Qian and Yuqing Feng
Water 2025, 17(22), 3221; https://doi.org/10.3390/w17223221 - 11 Nov 2025
Viewed by 553
Abstract
In eutrophic shallow lakes, dissolved oxygen (DO) exhibits significant temporal variations, regulated by the combined effects of photosynthesis and water temperature (WT). High-frequency monitoring enables a detailed capture of DO diel cycles, providing a more comprehensive understanding of the dynamic changes within lake [...] Read more.
In eutrophic shallow lakes, dissolved oxygen (DO) exhibits significant temporal variations, regulated by the combined effects of photosynthesis and water temperature (WT). High-frequency monitoring enables a detailed capture of DO diel cycles, providing a more comprehensive understanding of the dynamic changes within lake ecosystems. This study involved high-frequency (10 min intervals) in situ monitoring of DO over a three-year period (2020–2022) in the littoral zone of Taihu Lake, China. Random forest regression analysis identified WT, photosynthetically active radiation (PAR), and relative humidity (RH) as the three most influential variables governing DO dynamics. The relative importance of these factors varied seasonally (0.117–0.392), with PAR dominating in summer (0.383), whereas WT had the highest importance in other seasons (0.312–0.392). Cusum analysis further revealed that the DO-WT relationship changed from a dome-shaped pattern in spring, autumn, and winter to a bowl-shaped pattern in summer, indicating that thermal stratification intensified oxygen gradients. In addition, the majority of DO recovery occurred in the late afternoon during summer, suggesting that severe oxygen consumption delayed the daytime accumulation of DO. Our findings emphasize the critical roles of photosynthesis, respiration, and abiotic factors in shaping DO dynamics. This research enhances our understanding of DO fluctuations in eutrophic shallow lakes and provides valuable insights for ecosystem management, supporting the development of effective strategies to prevent and mitigate hypoxia. Full article
(This article belongs to the Special Issue Water Quality Monitoring and Prediction Using New Sensors, Machine Learning and Big Data)
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27 pages, 3723 KB  
Article
Research on Surface Water State for Rivers in Western Ukraine Using Time Series Forecasting Methods
by Leonid Bytsyura, Nina Szczepanik-Scislo, Oksana Desyatnyuk, Natalya Shakhovska, Lukasz Scislo, Anatoliy Sachenko, Olena Lototska, Ihor Shevchuk and Oksana Sofinska
Water 2025, 17(21), 3148; https://doi.org/10.3390/w17213148 - 2 Nov 2025
Cited by 1 | Viewed by 711
Abstract
This study presents a data-driven forecasting framework for surface water state trends using time-series modelling based on hydrochemical monitoring data from the Ikva River (Ukraine). The monitoring campaign, conducted between 2021 and 2023, involved monthly sampling of 19 hydrochemical indicators at two sites. [...] Read more.
This study presents a data-driven forecasting framework for surface water state trends using time-series modelling based on hydrochemical monitoring data from the Ikva River (Ukraine). The monitoring campaign, conducted between 2021 and 2023, involved monthly sampling of 19 hydrochemical indicators at two sites. We applied the Prophet time series forecasting algorithm, a decomposable additive model, to predict key indicators, including water hardness and bicarbonate concentration. The approach provides a transparent and adaptable method for forecasting water state in data-limited contexts. Key contributions include the integration of high-resolution hydrochemical monitoring with an explainable machine learning model, enabling early warning insights in under-monitored river basins. The case study of best-performing models for hydrocarbonate and hardness confirmed that Prophet offered well-calibrated prediction intervals with rapid deployment, high interpretability, and dependable uncertainty estimation, though its forecasts were comparatively less accurate. Analysis of computational performance shows that Prophet enables faster implementation and quick insights, while ARIMA and LSTM achieve higher predictive accuracy at the cost of longer execution times. Results demonstrate strong predictive skill: for hardness, MAE = 1.64 and RMSE = 1.73; for bicarbonate, MAE = 54.82 and RMSE = 62.00. Coverage accuracy of 95% prediction intervals exceeded 91% for both indicators. The proposed approach provides a practical foundation for implementing early-warning systems and supporting evidence-based water resource management in regions lacking real-time monitoring infrastructure. Full article
(This article belongs to the Special Issue Water Quality Monitoring and Prediction Using New Sensors, Machine Learning and Big Data)
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23 pages, 3226 KB  
Article
Advanced Flow Detection Cell for SPEs for Enhancing In Situ Water Monitoring of Trace Levels of Cadmium
by Giulia Mossotti, Davide Girelli, Matilde Aronne, Giulio Galfré, Andrea Piscitelli, Luciano Scaltrito, Sergio Ferrero and Valentina Bertana
Water 2025, 17(16), 2384; https://doi.org/10.3390/w17162384 - 12 Aug 2025
Viewed by 3279
Abstract
An advanced anodic stripping voltammetry (ASV)-based Micro Electro Mechanical System (MEMS) sensor for cadmium (Cd) detection is presented in this study, which is cost-effective and efficient for in situ water monitoring, providing a crucial early warning mechanism, streamlining environmental monitoring, and facilitating timely [...] Read more.
An advanced anodic stripping voltammetry (ASV)-based Micro Electro Mechanical System (MEMS) sensor for cadmium (Cd) detection is presented in this study, which is cost-effective and efficient for in situ water monitoring, providing a crucial early warning mechanism, streamlining environmental monitoring, and facilitating timely intervention to safeguard public health and environmental safety. The rationale behind this work is to address the critical need for an in situ monitoring system for cadmium (Cd) in freshwater sources, particularly those adjacent to agricultural fields. Cd(II) is a highly toxic heavy metal that poses a significant threat to agricultural ecosystems and human health due to its rapid bioaccumulation in plants and subsequent entry into the food chain. The developed analytic device is composed of a commercial mercury salt-modified graphite screen-printed electrode (SPE) with a custom-designed innovative polydimethylsiloxane (PDMS) flow detection cell. The flow cell was prototyped using 3D printing and replica moulding, with its design and performance validated through COMSOL Multiphysics simulations to optimize inflow conditions and ensure maximum analyte dispersion on the working electrode surface. Chemical detection was performed using square wave voltammetry, demonstrating a linear response for Cd(II) concentrations of 0 to 20 µg/L. The system exhibited robust analytical performance, enabling 25–30 daily analyses with consistent sensitivity within the Limit of Detection (LoD) set by the law of 3 µg/L. Full article
(This article belongs to the Special Issue Water Quality Monitoring and Prediction Using New Sensors, Machine Learning and Big Data)
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17 pages, 6176 KB  
Article
LSTM-Based Forecasting of Coastal Hypoxia in South Korea: Evaluating the Roles of Tide Level and Model Architecture
by Seongsik Park, Sung-Eun Park and Kyunghoi Kim
Water 2025, 17(11), 1622; https://doi.org/10.3390/w17111622 - 27 May 2025
Cited by 1 | Viewed by 1084
Abstract
Forecasting coastal bottom dissolved oxygen (DO) concentrations is essential for hypoxia mitigation and ecosystem protection, however it remains challenging due to the complex interplay of physical and biogeochemical drivers. This study proposes a novel two-stage long short-term memory (LSTM) modeling framework for forecasting [...] Read more.
Forecasting coastal bottom dissolved oxygen (DO) concentrations is essential for hypoxia mitigation and ecosystem protection, however it remains challenging due to the complex interplay of physical and biogeochemical drivers. This study proposes a novel two-stage long short-term memory (LSTM) modeling framework for forecasting bottom DO in Gamak Bay, Korea—a semi-enclosed bay prone to frequent summer hypoxia. The two-stage framework separately forecasts bottom DO and other environmental variables, allowing the model to better focus on bottom DO while more effectively incorporating tide level predicted via harmonic decomposition. The model’s performance was evaluated across four configurations, considering the inclusion or exclusion of tide level as a predictor and comparing one-stage and two-stage LSTM architectures. Multi-year in situ hourly observations (2017–2023) and tide level calculated by harmonic decomposition were used for model training and evaluation. Results showed that incorporating tide level substantially improved long-term forecasting performance, especially when combined with the two-stage LSTM architecture. The two-stage LSTM with tide level achieved the highest accuracy for 120 h forecasts (RMSE = 1.6 mg/L). These findings highlight the critical role of tidal dynamics in hypoxia forecasting and offer guidance for improving hypoxia forecasting strategies in coastal environments. Full article
(This article belongs to the Special Issue Water Quality Monitoring and Prediction Using New Sensors, Machine Learning and Big Data)
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