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Special Issue "Optimization and Prediction of Water Quality Model Based on Artificial Intelligence"
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 June 2023 | Viewed by 15105
Special Issue Editors
Interests: integrated stormwater management; urban diffuse pollution
Special Issues, Collections and Topics in MDPI journals
Interests: water resources management; forecasting with intelligent modelling; big data techniques and application
Special Issue Information
Natural water qualities such as lakes, streams, and estuaries are influenced by anthropogenic activities, and water deterioration and the need for further treatment is one of the direst and most worrisome issues. Accurate water quality prediction helps to implement early warning decision activities, which are usually considered a cost-effective and alternative water management measure. Traditional process-based models are the main tools for water pollution prediction, which could provide a coherent prediction of pollutant transport and distribution in time and space. However, it is difficult or less accurate to predict pollutants with traditional models due to the complex physical–chemical process-induced uncertainty of parameter values and the complexity of the simulation.
A recent big wave in machine learning has led to massive successes in different research matrices by leveraging large amounts of training data. Machine learning approaches have shown great abilities to extract featured information and identify the inherent correlations and patterns among complex datasets. However, the effectiveness, reliability, accuracy, as well as usability of machine learning algorithms in optimization and prediction of water quality are still largely unexplored.
Accordingly, the primary purpose of this Special Issue is to provide recent studies on novel machine learning approaches for tackling problems in water supply/distribution systems, river networks, water quality assessment, classical and emerging pollutant transportation, etc. Theoretical and practical advancements in physics-informed and/or theory-guided machine learning approaches are also welcomed.
Prof. Dr. Jin Zhang
Prof. Dr. Yun Bai
Prof. Dr. Pei Hua
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 2200 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.
- deep learning tools
- novel machine learning algorithms
- intelligent forecasting
- uncertainty quantification
- neural networks
- water supply/distribution systems
- data-driven techniques
- water quality model
- predicting classical and emerging contaminants
- low carbon–water quality-based forecasting and decision making