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Water
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Monitoring and Forecasting Technologies for Marine Environments and Hazards
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Monitoring and Forecasting Technologies for Marine Environments and Hazards

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Oceans and Coastal Zones".

Deadline for manuscript submissions: 15 January 2025 | Viewed by 1892

Special Issue Editor

Dr. Chih-Chieh Young

E-Mail Website
Guest Editor
Department of Marine Environmental Informatics, National Taiwan Ocean University, Keelung, Taiwan
Interests: nonlinear wave dynamics; coastal oceanography; computational fluid dynamics; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Marine environmental impacts and disasters due to global warming and climate change are some of the most critical issues in this century. The management of marine environments and reductions in coastal disasters with the development and application of key technologies in observations, ocean models, big data and artificial intelligence approaches are crucial. This Special Issue “Monitoring and Forecasting Technologies for Marine Environments and Hazards” invites research papers with focuses on observational databases, forecasting techniques, and marine disasters and management. The topics listed below and other related works are welcome:

  • Long-term observation declaring the impacts of climate changes on the marine environment.
  • Developments and applications of observational techniques like remote sensing (radar and satellite).
  • Developments and applications of forecasting technology including ocean models as well as big data and artificial intelligence approaches.
  • Evaluation and management of marine environmental, biological, geochemical, and ecological disasters.

Dr. Chih-Chieh Young
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

  • ocean circulation
  • tides and waves
  • biological and geochemical processes
  • field observation and remote sensing
  • numerical modelling
  • big data and artificial intelligence
  • marine environments and hazards

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

Published Papers (2 papers)

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Research

22 pages, 7895 KiB  
Article
Spatiotemporal Fusion Prediction of Sea Surface Temperatures Based on the Graph Convolutional Neural and Long Short-Term Memory Networks
by Jingjing Liu, Lei Wang, Fengjun Hu, Ping Xu and Denghui Zhang
Water 2024, 16(12), 1725; https://doi.org/10.3390/w16121725 - 18 Jun 2024
Viewed by 634
Abstract
Sea surface temperature (SST) prediction plays an important role in scientific research, environmental protection, and other marine-related fields. However, most of the current prediction methods are not effective enough to utilize the spatial correlation of SSTs, which limits the improvement of SST prediction [...] Read more.
Sea surface temperature (SST) prediction plays an important role in scientific research, environmental protection, and other marine-related fields. However, most of the current prediction methods are not effective enough to utilize the spatial correlation of SSTs, which limits the improvement of SST prediction accuracy. Therefore, this paper first explores spatial correlation mining methods, including regular boundary division, convolutional sliding translation, and clustering neural networks. Then, spatial correlation mining through a graph convolutional neural network (GCN) is proposed, which solves the problem of the dependency on regular Euclidian space and the lack of spatial correlation around the boundary of groups for the above three methods. Based on that, this paper combines the spatial advantages of the GCN and the temporal advantages of the long short-term memory network (LSTM) and proposes a spatiotemporal fusion model (GCN-LSTM) for SST prediction. The proposed model can capture SST features in both the spatial and temporal dimensions more effectively and complete the SST prediction by spatiotemporal fusion. The experiments prove that the proposed model greatly improves the prediction accuracy and is an effective model for SST prediction. Full article
(This article belongs to the Special Issue Monitoring and Forecasting Technologies for Marine Environments and Hazards)
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21 pages, 13733 KiB  
Article
Monsoons and Tide-Induced Eddies Deflect the Dispersion of the Thermal Plume in Nan Wan Bay
by Hung-Jen Lee, Shih-Jen Huang, Pei-Jie Meng, Chung-Chi Chen, Chia-Ying Ho and Yi-Chen Tsai
Water 2024, 16(10), 1420; https://doi.org/10.3390/w16101420 - 16 May 2024
Viewed by 680
Abstract
The present work employs a three-dimensional ocean model (MITgcm) driven by tidal and climatological forcings to assess the range of impacts of thermal wastewater discharge from the Third Nuclear Power Plant (NP_No.3) in Nan Wan Bay on the local ecosystem. Tides and daily [...] Read more.
The present work employs a three-dimensional ocean model (MITgcm) driven by tidal and climatological forcings to assess the range of impacts of thermal wastewater discharge from the Third Nuclear Power Plant (NP_No.3) in Nan Wan Bay on the local ecosystem. Tides and daily wind forcings are incorporated into the MITgcm to examine their effects on thermal plume dispersion and water circulation in Nan Wan Bay. The model results reveal that the thermal plume is most likely to disperse to the southwest in the summer; it is unlikely to drift to the southeast or northeast because of the presence of the gentle southwesterly monsoon. In the winter, the thermal plume is most likely to be directed to the southwest and is unlikely to be directed to the northeast or southeast because of the prevailing northeasterly monsoon. Additionally, it is worth emphasizing that strong tidal currents generate a pair of counter-rotating eddies that significantly influence the dispersion of the thermal plume. However, seasonal monsoons also play an essential role in modifying the thermal plume’s direction and dispersion. Full article
(This article belongs to the Special Issue Monitoring and Forecasting Technologies for Marine Environments and Hazards)
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