Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.0
3.0
Journals
Water
Special Issues
Water Environment Modeling, Simulation, Informatics, and Big Data Mining
water-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Water Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Water Environment Modeling, Simulation, Informatics, and Big Data Mining

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: 30 June 2026 | Viewed by 2288

Special Issue Editors

Dr. Yulin Kang

E-Mail Website
Guest Editor
Chinese Research Academy of Environmental Sciences, Beijing, China
Interests: environmental informatization; environmental big data mining
Prof. Baiyin Liu

E-Mail Website
Guest Editor
Chinese Research Academy of Environmental Sciences, Beijing, China
Interests: environmental informatics; water model; water pollution tracing; water system platform
Dr. Yan Chen

E-Mail
Guest Editor
Chinese Research Academy of Environmental Sciences, Beijing, China
Interests: water environmental research; hydrodynamics; water ecology; simulation

Special Issue Information

Dear Colleagues,

In the current phase of deep integration between digital intelligence and ecological civilization, emerging information technologies such as 5G and artificial intelligence (AI) are fundamentally transforming the underlying frameworks of environmental governance. These advancements are driving significant changes in the monitoring, simulation, management, and evaluation of water environments. The widespread application of modeling, simulation, and big data technologies in water environmental research is providing invaluable insights into the complex interdependencies between water environments, water ecology, and water resources. This Special Issue seeks to explore the latest developments and pioneering applications at the intersection of informatics and water environmental science, with a particular emphasis on how advanced modeling and simulation tools, coupled with data-driven methodologies, can deepen our understanding of water systems and enhance decision-making in water resource management.

The topics covered in this Special Issue include, but are not limited to the following:

(1) Simulation analysis of water environments and hydrodynamic modeling;

(2) Collection, processing, and mining of water environment big data;

(3) Innovations and applications in water environment monitoring technologies;

(4) The application of machine learning and artificial intelligence in water environmental science;

(5) Real-world applications of water environment modeling and simulation;

(6) Emerging paradigms in intelligent and digital governance of water environments;

(7) Development of informatics software for water environments and integration of hardware–software systems;

(8) The use of digital twin technology in water environment management;

(9) Big data-driven risk assessment and predictive modeling of water environments.

This Special Issue aims to serve as a platform for advancing the field of water environment informatics, fostering the exchange of cutting-edge scientific knowledge, and promoting the implementation of innovative solutions.

Dr. Yulin Kang
Prof. Baiyin Liu
Dr. Yan Chen
Guest Editors

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 environment
  • environmental monitoring
  • environmental informatization
  • environmental big data mining
  • environmental modeling
  • environmental simulation
  • AI application
  • environmental data analysis

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

13 pages, 3668 KB  
Article
Prediction of Red Tide Occurrence Using Integrated Machine-Learning Algorithms—A Case in Hong Kong Coastal Waters
by Lifen Yao, Lei Zhu, Zeda Song, Yuxuan Wu, Xi Wang, Jiao Dong and Yulin Kang
Water 2026, 18(3), 374; https://doi.org/10.3390/w18030374 - 1 Feb 2026
Cited by 1 | Viewed by 1046
Abstract
Red tides are among the most destructive marine ecological hazards worldwide, posing significant threats to fisheries, biodiversity, and human health. Therefore, it is imperative to accurately and timely predict red tide occurrences to mitigate their ecological and socioeconomic impacts. However, the prediction accuracy [...] Read more.
Red tides are among the most destructive marine ecological hazards worldwide, posing significant threats to fisheries, biodiversity, and human health. Therefore, it is imperative to accurately and timely predict red tide occurrences to mitigate their ecological and socioeconomic impacts. However, the prediction accuracy of red tides is challenged by the complex, nonlinear relationships between red tide algae and environmental factors. Using 35 years (1986–2020) of continuous in situ records of water quality and red tides in Hong Kong coastal waters, this study developed an integrated prediction framework based on five machine-learning algorithms: Random Forest, Back-Propagation Neural Network, Support Vector Machine, Gaussian Naive Bayes, and Logistic Regression. After feature selection using the Granger causality test and variance inflation factor, the random forest algorithm achieved the highest individual-model accuracy of 84.85% for predicting red tide occurrence. An integrated model combining the top three algorithms further improved performance, reaching an accuracy of 98.5%. Feature-importance analyses indicated that silicon (Si) and suspended solids (SS) are the most influential environmental predictors in the integrated model. Overall, this study provides a high-precision and interpretable framework for predicting red tide occurrence and offers new insights into the environmental mechanisms underlying red tide outbreaks. Full article
(This article belongs to the Special Issue Water Environment Modeling, Simulation, Informatics, and Big Data Mining)
►▼ Show Figures

Figure 1

23 pages, 6131 KB  
Article
Integration of Snowmelt Runoff Model (SRM) with GIS and Remote Sensing for Operational Forecasting in the Kırkgöze Watershed, Turkey
by Serkan Şenocak and Reşat Acar
Water 2026, 18(3), 335; https://doi.org/10.3390/w18030335 - 29 Jan 2026
Viewed by 730
Abstract
Accurate snowmelt runoff prediction is critical for water resource management in mountainous regions where seasonal snowpack constitutes the dominant water supply. This study demonstrates operational application of the degree-day-based Snowmelt Runoff Model (SRM) integrated with Geographic Information Systems (GIS) and multi-platform remote sensing [...] Read more.
Accurate snowmelt runoff prediction is critical for water resource management in mountainous regions where seasonal snowpack constitutes the dominant water supply. This study demonstrates operational application of the degree-day-based Snowmelt Runoff Model (SRM) integrated with Geographic Information Systems (GIS) and multi-platform remote sensing for discharge forecasting in the Kirkgoze Basin (242.7 km2, 1823–3140 m elevation), Eastern Anatolia, Turkey. Three automatic weather stations spanning 872 m elevation gradient provided meteorological forcing, while MODIS MOD10A2 8-day composite products supplied operational snow cover observations validated against Landsat-5/7 (30 m resolution, 87.3% agreement, Kappa = 0.73) and synthetic aperture radar imagery (RADARSAT-1 C-band, ALOS-PALSAR L-band). Uncalibrated model performance was modest (R2 = 0.384, volumetric difference = 29.78%), demonstrating necessity of site-specific calibration. Systematic adjustment of snowmelt and rainfall runoff coefficients yielded excellent calibrated performance for 2009 melt season: R2 = 0.8606, correlation coefficient R = 0.927, Nash–Sutcliffe efficiency = 0.854, and volumetric difference = 3.35%. Enhanced temperature lapse rate (0.75 °C/100 m vs. standard 0.65 °C/100 m) reflected severe continental climate. Multiple linear regression analysis identified temperature, snow-covered area, snow water equivalent, and calibrated runoff coefficients as significant discharge predictors (R2 = 0.881). Results confirm SRM’s operational feasibility for seasonal forecasting and flood warning in data-scarce snow-dominated basins, with modest requirements (daily temperature, precipitation, and satellite snow cover) aligning with operational monitoring capabilities. The methodology provides a transferable framework for regional water resource management in climatically vulnerable mountain environments where snowmelt supports agriculture, hydropower, and municipal supply. Full article
(This article belongs to the Special Issue Water Environment Modeling, Simulation, Informatics, and Big Data Mining)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop