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Computation
Special Issues
Modeling Study of Hydrodynamic Environmental Impact
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Modeling Study of Hydrodynamic Environmental Impact

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 3941

Special Issue Editor

Dr. Ting-Chieh Lin

E-Mail Website
Guest Editor
Department of Harbor and River Engineering, National Taiwan Ocean University, Keelung, Taiwan
Interests: wave hydrodynamics; environmental pollutants; numerical model; wave mechanics; numerical mathematics

Special Issue Information

Dear Colleagues,

The application of numerical model for investigating coastal area, estuary and deep water region has been widely used to investigate the environmental impact.  This Special Issue is dedicated to exploring the recent advances in various modeling applications of the hydrodynamic environmental impact. The interests are in modeling methodology, engineering application and modeling strategy. Papers may report on original research, discuss methodological aspects, review the current state of the art, or offer perspectives on future prospects.

Specific methods and fields of applications include, but are not limited to:

  • Numerical method and theory;
  • Coastal engineering;
  • Estuary hydrodynamics;
  • Sediment transport;
  • Oil-spill transport;
  • Particle tracking;
  • Tidal flushing;
  • Water quality;

Dr. Ting-Chieh Lin
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. Computation is an international peer-reviewed open access monthly 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 1800 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

  • hydrodynamic model
  • coastal engineering
  • estuary
  • water quality
  • environmental impact

Published Papers (1 paper)

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Research

14 pages, 533 KiB  
Article
Efficient Data-Driven Machine Learning Models for Water Quality Prediction
by Elias Dritsas and Maria Trigka
Computation 2023, 11(2), 16; https://doi.org/10.3390/computation11020016 - 18 Jan 2023
Cited by 13 | Viewed by 3570
Abstract
Water is a valuable, necessary and unfortunately rare commodity in both developing and developed countries all over the world. It is undoubtedly the most important natural resource on the planet and constitutes an essential nutrient for human health. Geo-environmental pollution can be caused [...] Read more.
Water is a valuable, necessary and unfortunately rare commodity in both developing and developed countries all over the world. It is undoubtedly the most important natural resource on the planet and constitutes an essential nutrient for human health. Geo-environmental pollution can be caused by many different types of waste, such as municipal solid, industrial, agricultural (e.g., pesticides and fertilisers), medical, etc., making the water unsuitable for use by any living being. Therefore, finding efficient methods to automate checking of water suitability is of great importance. In the context of this research work, we leveraged a supervised learning approach in order to design as accurate as possible predictive models from a labelled training dataset for the identification of water suitability, either for consumption or other uses. We assume a set of physiochemical and microbiological parameters as input features that help represent the water’s status and determine its suitability class (namely safe or nonsafe). From a methodological perspective, the problem is treated as a binary classification task, and the machine learning models’ performance (such as Naive Bayes–NB, Logistic Regression–LR, k Nearest Neighbours–kNN, tree-based classifiers and ensemble techniques) is evaluated with and without the application of class balancing (i.e., use or nonuse of Synthetic Minority Oversampling Technique–SMOTE), comparing them in terms of Accuracy, Recall, Precision and Area Under the Curve (AUC). In our demonstration, results show that the Stacking classification model after SMOTE with 10-fold cross-validation outperforms the others with an Accuracy and Recall of 98.1%, Precision of 100% and an AUC equal to 99.9%. In conclusion, in this article, a framework is presented that can support the researchers’ efforts toward water quality prediction using machine learning (ML). Full article
(This article belongs to the Special Issue Modeling Study of Hydrodynamic Environmental Impact)
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