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Application of Artificial Intelligence (AI) in Water Quality Monitoring
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Application of Artificial Intelligence (AI) in Water Quality Monitoring

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 September 2025 | Viewed by 4976

Special Issue 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 water resource sustainable utilization, security, and aquatic ecological environment. By monitoring water quality, pollutants, bacteria, and other harmful substances in water bodies can be detected and identified early on, and it helps to take corresponding measures to protect public health and ecosystems. Water quality monitoring also helps to assess the sustainability of water resources and guide rational water resource management and decision making. The rapidly developing artificial intelligence technology in 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, which 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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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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Published Papers (4 papers)

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Research

32 pages, 17827 KiB  
Article
Trends in Coral Reef Habitats over Two Decades: Lessons Learned from Nha Trang Bay Marine Protected Area, Vietnam
by Nguyen Trinh Duc Hieu, Nguyen Hao Quang, Tran Duc Dien, Vo Thi Ha, Nguyen Dang Huyen Tran, Tong Phuoc Hoang Son, Tri Nguyen-Quang, Tran Thi Thuy Hang and Ha Nam Thang
Water 2025, 17(8), 1224; https://doi.org/10.3390/w17081224 - 19 Apr 2025
Viewed by 373
Abstract
Coral reefs are well known for their diversity and value, providing habitats for a third of marine species within just 0.2% of the ocean. However, these natural habitats face significant threats and degradation, leading to unresolved issues related to coral loss inventory, coral [...] Read more.
Coral reefs are well known for their diversity and value, providing habitats for a third of marine species within just 0.2% of the ocean. However, these natural habitats face significant threats and degradation, leading to unresolved issues related to coral loss inventory, coral protection, and the implementation of long-term conservation policies. In this study, we examined two decades of changes in coral spatial distribution within the Nha Trang Bay Marine Protected Area (MPA) using remote sensing and machine learning (ML) approaches. We identified various factors contributing to coral reef loss and analyzed the effectiveness of management policies over the past 20 years. By employing the Light Gradient Boosting Machine (LGBM) and Deep Forest (DF) models on Landsat (2002, κ = 0.83, F1 = 0.85) and Planet (2016, κ = 0.89, F1 = 0.82; 2024, κ = 0.92, F1 = 0.86) images, we achieved high confidence in our inventory of coral changes. Our findings revealed that 191.38 hectares of coral disappeared from Nha Trang Bay MPA between 2002 and 2024. The 8-year period from 2016 to 2024 saw a loss of 66.32 hectares, which is in linear approximation to the 125.06 hectares lost during the 14-year period from 2002 to 2016. It is concluded that the key factors contributing to coral loss include land-use dynamics, global warming, and the impact of starfish. To address these challenges, we propose next a modern community-based management paradigm to enhance the conservation of existing coral reefs and protect potential habitats within Nha Trang Bay MPA. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence (AI) in Water Quality Monitoring)
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12 pages, 2365 KiB  
Article
Artificial Neural Networks (ANNs) and Multiple Linear Regression (MLR) Analysis Modelling for Predicting Chemical Dosages of a Water Treatment Plant (WTP) of Drinking Water
by Stylianos Gyparakis, Ioannis Trichakis, Tryfon Daras and Evan Diamadopoulos
Water 2025, 17(2), 227; https://doi.org/10.3390/w17020227 - 16 Jan 2025
Viewed by 750
Abstract
As the quantity and quality of water resources decreases, the need for timely and valid prediction of the WTP of drinking water-used chemicals to produce quality drinking water for the final consumer increases. The question that arises is which prediction model performs better [...] Read more.
As the quantity and quality of water resources decreases, the need for timely and valid prediction of the WTP of drinking water-used chemicals to produce quality drinking water for the final consumer increases. The question that arises is which prediction model performs better in predicting the chemical dosages used in a WTP of drinking water. ANNs or the MLR analysis models? The present study is a comparative study between the two aforementioned prediction models. The evaluation criteria chosen are: the Root Mean Square Error (RMSE), the Coefficient of Determination (R2), and the Pearson Correlation Coefficient (R). A previously optimised ensemble ANN model was chosen, which consisted of 100 neural networks, with 42 hidden nodes each, 10 inputs, and 4 outputs. On the other hand, four different scenarios in MLR analysis with dependent variables were examined: the ozone (O3) concentration, the Anionic Polyelectrolyte (ANPE) dosage, the Poly-Aluminium Chloride hydroxide sulphate (PACl) dosage, and the chlorine (Cl2(g)) dosage. As independent variables, 10 WTP operational and quality water variables were considered. According to RMSE results, the MLR model had better performance for the three (RMSE ANPE = 0.05 mg/L, RMSE PACl = 0.08 mg/L, and RMSE Cl2(g) = 0.10 kg/h) of the four used WTP of drinking water chemicals, than the ANN model, which performed better for only one (RMSE O3 = 0.02 mg/L). According to R2 and R results, the ANN model had better performance compared to the MLR analysis model for all four variables. Based on the criterion of R2 > 0.5, the ANN performance was satisfactory in predicting three variables: ANPE (R2 = 0.772), PACl (R2 = 0.742), and Cl2(g) dosage (R2 = 0.838, +23% and R = 0.91553, +11%). Respectively, the prediction of the MLR analysis model was evaluated as satisfactory only for the Cl2(g) dosage (R2 = 0.681, R = 0.82500). If someone wants to use the above described (ANN or MLR) scenarios to predict Cl2(g) dosages, it is better to use the one with the smallest RMSE. If they are interested in fitting purposes, the one with the largest R2, is preferable. Also, the ozone concentration variable showed low values of the R2, in all cases, possibly due to the large variation in its values. This study further strengthens the opinion that ANNs are useful decision support tools for a WTP of a drinking water operator and can accurately and sufficiently mimic the decisions regarding the used chemical dosages, which is the main daily concern of the plant operator. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence (AI) in Water Quality Monitoring)
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Graphical abstract

22 pages, 4749 KiB  
Article
A Hybrid Model Combined Deep Neural Network and Beluga Whale Optimizer for China Urban Dissolved Oxygen Concentration Forecasting
by Tianruo Wang, Linzhi Ding, Danyi Zhang and Jiapeng Chen
Water 2024, 16(20), 2966; https://doi.org/10.3390/w16202966 - 17 Oct 2024
Cited by 2 | Viewed by 1134
Abstract
The dissolved oxygen concentration (DOC) is an important indicator of water quality. Accurate DOC predictions can provide a scientific basis for water environment management and pollution prevention. This study proposes a hybrid DOC forecasting framework combined with Variational Mode Decomposition (VMD), a convolutional [...] Read more.
The dissolved oxygen concentration (DOC) is an important indicator of water quality. Accurate DOC predictions can provide a scientific basis for water environment management and pollution prevention. This study proposes a hybrid DOC forecasting framework combined with Variational Mode Decomposition (VMD), a convolutional neural network (CNN), a Gated Recurrent Unit (GRU), and the Beluga Whale Optimization (BWO) algorithm. Specifically, the original DOC sequences were decomposed using VMD. Then, CNN-GRU combined with an attention mechanism was utilized to extract the key features and local dependency of the decomposed sequences. Introducing the BWO algorithm solved the correction coefficients of the proposed system, with the aim of improving prediction accuracy. This study used 4-h monitoring China urban water quality data from November 2020 to November 2023. Taking Lianyungang as an example, the empirical findings exhibited noteworthy enhancements in performance metrics such as MSE, RMSE, MAE, and MAPE within the VMD-BWO-CNN-GRU-AM, with reductions of 0.2859, 0.3301, 0.2539, and 0.0406 compared to a GRU. These results affirmed the superior precision and diminished prediction errors of the proposed hybrid model, facilitating more precise DOC predictions. This proposed DOC forecasting system is pivotal for sustainably monitoring and regulating water quality, particularly in terms of addressing pollution concerns. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence (AI) in Water Quality Monitoring)
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17 pages, 4531 KiB  
Article
Using Artificial Neural Networks to Predict Operational Parameters of a Drinking Water Treatment Plant (DWTP)
by Stylianos Gyparakis, Ioannis Trichakis and Evan Diamadopoulos
Water 2024, 16(19), 2863; https://doi.org/10.3390/w16192863 - 9 Oct 2024
Cited by 1 | Viewed by 1441
Abstract
The scope of the present study is the estimation of key operational parameters of a drinking water treatment plant (DWTP), particularly the dosages of treatment chemicals, using artificial neural networks (ANNs) based on measurable in situ data. The case study consists of the [...] Read more.
The scope of the present study is the estimation of key operational parameters of a drinking water treatment plant (DWTP), particularly the dosages of treatment chemicals, using artificial neural networks (ANNs) based on measurable in situ data. The case study consists of the Aposelemis DWTP, where the plant operator had an estimation of the ANN output parameters for the required dosages of water treatment chemicals based on observed water quality and other operational parameters at the time. The estimated DWTP main operational parameters included residual ozone (O3) and dosages of the chemicals used: anionic polyelectrolyte (ANPE), poly-aluminum chloride hydroxide sulfate (PACl), and chlorine gas (Cl2(g)). Daily measurable results of water sample analysis and recordings from the DWTP Supervisory Control and Data Acquisition System (SCADA), covering a period of 38 months, were used as input parameters for the artificial neural network (1188 values for each of the 14 measurable parameters). These input parameters included: raw water supply (Q), raw water turbidity (T1), treated water turbidity (T2), treated water residual free chlorine (Cl2), treated water concentration of residual aluminum (Al), filtration bed inlet water turbidity (T3), daily difference in water height in reservoir (∆H), raw water pH (pH1), treated water pH (pH2), and daily consumption of DWTP electricity (El). Output/target parameters were: residual O3 after ozonation (O3), anionic polyelectrolyte (ANPE), poly-aluminum chloride hydroxide sulfate (PACl), and chlorine gas supply (Cl2(g)). A total of 304 different ANN models were tested, based on the best test performance (tperf) indicator. The one with the optimum performance indicator was selected. The scenario finally chosen was the one with 100 neural networks, 100 nodes, 42 hidden nodes, 10 inputs, and 4 outputs. This ANN model achieved excellent simulation results based on the best testing performance indicator, which suggests that ANNs are potentially useful tools for the prediction of a DWTP’s main operational parameters. Further research could explore the prediction of water chemicals used in a DWTP by using ANNs with a smaller number of operational parameters to ensure greater flexibility, without prohibitively reducing the reliability of the prediction model. This could prove useful in cases with a much higher sample size, given the data-demanding nature of ANNs. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence (AI) in Water Quality Monitoring)
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