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Water
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Advanced Technologies for Water Quality Monitoring and Prediction
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Advanced Technologies for Water Quality Monitoring and Prediction

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: 29 April 2024 | Viewed by 4161

Special Issue Editor

Dr. Yong Jie Wong

E-Mail Website
Guest Editor
Department of Bioenvironmental Design, Faculty of Bioenvironmental Science, Kyoto University of Advance Science, Kyoto, Japan
Interests: machine learning; statistical analysis; water pollution; geographic information science; climate change; hydrological modeling

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;
  • Use of the Internet of Things (IoT) in real-time water quality monitoring and control;
  • Environmental monitoring and water resource management for sustainable development;
  • Application 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. The articles will 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) for sustainable water resource management. The 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. The Special Issue will contribute to the advancement of 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 100 words) can be sent to the Editorial Office for announcement on this website.

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

Published Papers (3 papers)

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Research

15 pages, 5201 KiB  
Article
Daily Runoff Prediction with a Seasonal Decomposition-Based Deep GRU Method
by Feifei He, Qinjuan Wan, Yongqiang Wang, Jiang Wu, Xiaoqi Zhang and Yu Feng
Water 2024, 16(4), 618; https://doi.org/10.3390/w16040618 - 19 Feb 2024
Viewed by 803
Abstract
Accurately predicting hydrological runoff is crucial for water resource allocation and power station scheduling. However, there is no perfect model that can accurately predict future runoff. In this paper, a daily runoff prediction method with a seasonal decomposition-based-deep gated-recurrent-unit (GRU) method (SD-GRU) is [...] Read more.
Accurately predicting hydrological runoff is crucial for water resource allocation and power station scheduling. However, there is no perfect model that can accurately predict future runoff. In this paper, a daily runoff prediction method with a seasonal decomposition-based-deep gated-recurrent-unit (GRU) method (SD-GRU) is proposed. The raw data is preprocessed and then decomposed into trend, seasonal, and residual components using the seasonal decomposition algorithm. The deep GRU model is then used to predict each subcomponent, which is then integrated into the final prediction results. In particular, the hyperparameter optimization algorithm of tree-structured parzen estimators (TPE) is used to optimize the model. Moreover, this paper introduces the single machine learning model (including multiple linear regression (MLR), back propagation (BP), long short-term memory neural network (LSTM) and gate recurrent unit (GRU)) and a combination model (including seasonal decomposition–back propagation (SD-BP), seasonal decomposition–multiple linear regression (SD-MLR), along with seasonal decomposition–long-and-short-term-memory neural network (SD-LSTM), which are used as comparison models to verify the excellent prediction performance of the proposed model. Finally, a case study of the Qingjiang Shuibuya test set, which considers the period 1 January 2019 to 31 December 2019, is conducted. Case studies of the Qingjiang River show the proposed model outperformed the other models in prediction performance. The model achieved a mean absolute error (MAE) index of 38.5, a Nash-Sutcliffe efficiency (NSE) index of 0.93, and a coefficient of determination (R2) index of 0.7. In addition, compared to the comparison model, the NSE index of the proposed model increased by 19.2%, 19.2%, 16.3%, 16.3%, 2.2%, 2.2%, and 1.1%, when compared to BP, MLR, LSTM, GRU, SD-BP, SD-MLR, SD-LSTM, and SD-GRU, respectively. This research can provide an essential reference for the study of daily runoff prediction models. Full article
(This article belongs to the Special Issue Advanced Technologies for Water Quality Monitoring and Prediction)
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22 pages, 4110 KiB  
Article
Dissolved Oxygen Inversion Based on Himawari-8 Imagery and Machine Learning: A Case Study of Lake Chaohu
by Kaifang Shi, Peng Wang, Hang Yin, Qi Lang, Haozhi Wang and Guoxin Chen
Water 2023, 15(17), 3081; https://doi.org/10.3390/w15173081 - 28 Aug 2023
Cited by 2 | Viewed by 1162
Abstract
Dissolved oxygen (DO) concentration is a widely used and effective indicator for assessing water quality and pollution in aquatic environments. Continuous and large-scale inversion of water environments using remote sensing imagery has become a hot topic in water environmental research. Remote sensing technology [...] Read more.
Dissolved oxygen (DO) concentration is a widely used and effective indicator for assessing water quality and pollution in aquatic environments. Continuous and large-scale inversion of water environments using remote sensing imagery has become a hot topic in water environmental research. Remote sensing technology has been extensively applied in water quality monitoring, but its limited sampling frequency necessitates the development of a high-frequency dynamic water quality monitoring model. In this study, we utilized Lake Chaohu as a case study. Firstly, we constructed a dynamic water quality inversion model for monitoring DO concentrations using machine learning methods, with Himawari-8 (H8) satellite imagery as input data and DO concentrations in Lake Chaohu as output data. Secondly, the developed DO concentration inversion model was employed to estimate the overall grid-based DO concentration in the Lake Chaohu region for the years 2019 to 2021. Lastly, Pearson correlation analysis and significance tests were performed to examine the correlation and significance between the estimated grid-based DO concentration and the ERA5 reanalysis dataset. The results demonstrate that the Random Forest (RF) model performs best in DO concentration inversion, with a high R2 score of 0.84, and low RMSE and MAE values of 0.69 and 0.54, respectively. Compared to other models, the RF model improves average performance with a 38% increase in R2, 13% decrease in RMSE, and 33% decrease in MAE. The model accurately predicts DO concentrations. Furthermore, the inversion results reveal seasonal differences in DO concentrations in Lake Chaohu from 2019 to 2021, with higher concentrations in spring and winter, and lower concentrations in summer and autumn. The average DO concentrations in the northwest, central-south, and northeast regions of Lake Chaohu are 10.12 mg/L, 9.98 mg/L, and 9.96 mg/L, respectively, with higher concentrations in the northwest region. Pearson correlation analysis indicates a significant correlation (p < 0.01) between DO concentrations and temperature, surface pressure, latent heat flux from the atmosphere to the surface, and latent heat flux from the surface to the atmosphere, with correlation coefficients of −0.615, 0.583, −0.480, and 0.444, respectively. The results verify the feasibility of using synchronous satellites for real-time inversion of DO concentrations, providing a more efficient, economical, and accurate means for real-time monitoring of DO concentrations. This study has practical value in improving the efficiency and accuracy of water environmental monitoring. Full article
(This article belongs to the Special Issue Advanced Technologies for Water Quality Monitoring and Prediction)
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15 pages, 3186 KiB  
Article
Research on a Prediction Model of Water Quality Parameters in a Marine Ranch Based on LSTM-BP
by He Xu, Bin Lv, Jie Chen, Lei Kou, Hailin Liu and Min Liu
Water 2023, 15(15), 2760; https://doi.org/10.3390/w15152760 - 30 Jul 2023
Cited by 4 | Viewed by 1036
Abstract
Water quality is an important factor affecting marine pasture farming. Water quality parameters have the characteristics of time series, showing instability and nonlinearity. Previous water quality prediction models are usually based on specific assumptions and model parameters, which may have limitations for complex [...] Read more.
Water quality is an important factor affecting marine pasture farming. Water quality parameters have the characteristics of time series, showing instability and nonlinearity. Previous water quality prediction models are usually based on specific assumptions and model parameters, which may have limitations for complex water environment systems. Therefore, in order to solve the above problems, this paper combines long short-term memory (LSTM) and backpropagation (BP) neural networks to construct an LSTM-BP combined water quality parameter prediction model and uses the root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency coefficient (NSE) to evaluate the model. Experimental results show that the prediction performance of the LSTM-BP model is better than other models. On the RMSE and MAE indicators, the LSTM-BP model is 76.69% and 79.49% lower than other models, respectively. On the NSE index, the LSTM-BP model has improved by 34.13% compared with other models. The LSTM-BP model can effectively reflect time series characteristics and nonlinear mapping capabilities. This research provides a new method and reference for the prediction of water quality parameters in marine ranching and further enables the intelligent and sustainable development of marine ranching. Full article
(This article belongs to the Special Issue Advanced Technologies for Water Quality Monitoring and Prediction)
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