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Applied Sciences
Special Issues
Environmental Monitoring and Analysis for Hydrology
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Environmental Monitoring and Analysis for Hydrology

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: 20 August 2024 | Viewed by 5717

Special Issue Editors

Prof. Dr. Wei Song

E-Mail Website
Guest Editor
College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
Interests: marine big data analysis; computer vision; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Antonio Liotta

E-Mail Website
Guest Editor
Faculty of Computer Science, Free University of Bozen-Bolzano, 39100 Bolzano, Italy
Interests: data analsis; machine learning; smart systems
Special Issues, Collections and Topics in MDPI journals
Dr. Qi He

E-Mail Website
Guest Editor
College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
Interests: marine environmental monitoring and forecasting; remote sensing image classification

Special Issue Information

Dear Colleagues,

Environmental monitoring and analysis for hydrology is an active area of research and practice, as the quality and availability of water resources continue to be a pressing concern for communities around the world. The current status of research in this field reflects the growing recognition of the importance of comprehensive and integrated approaches to environmental monitoring and analysis, especially for water management and marine hydrological environment.

One of the current challenges in environmental monitoring and analysis for hydrology is the need to develop new and more effective technologies and methods for data collection and analysis. This includes the development of low-cost sensors and other instruments that can be deployed in remote or inaccessible locations, as well as the integration of new technologies like artificial intelligence and machine learning into data analysis. Another challenge is the need to improve our understanding of the complex interactions between water resources and other environmental systems, including the impacts of climate change, land use change, and pollution on aquatic ecosystems. This requires the development of more sophisticated models and analytical tools, as well as increased collaboration between experts in different fields.

This special issue invites the academic community and relevant industrial partners to submit high-quality papers to address these challenges and/or explore new solutions. Relevant topics include, but are not limited to, the following areas:

  • Innovative technologies and methods for environmental monitoring and data collection for hydrology
  • Integration of environmental data from multiple sources, including remote sensing imagery and in-situ monitoring
  • Development and application of environmental models for hydrological analysis and prediction
  • Impacts of climate change, land use change, and pollution on water resources, aquatic ecosystems and marine ecology
  • Environmental impact of ocean engineering such as offshore wind power
  • Data quality control of marine environmental monitoring
  • Data-driven methods for hydrodynamic environment forecasting and simulation
  • Optical and laser technologies for extreme environmental monitoring and analysis, such as deep sea and Polar Marine
  • Case studies and best practices in environmental monitoring and analysis for hydrology
  • Future directions for research and technology development in this field

Prof. Dr. Wei Song
Prof. Dr. Antonio Liotta
Dr. Qi He
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 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. Applied Sciences 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 2400 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

  • environmental monitoring for hydrology
  • emerging technologies for hydrological data analysis
  • hydrology and ecological environment monitoring and management
  • hydrological environment forcasting and simulation
  • data quality control
  • optical and laser technologies for extreme environmental monitoring and analysis

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

Published Papers (4 papers)

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Research

17 pages, 2543 KiB  
Article
Named Entity Recognition for Chinese Texts on Marine Coral Reef Ecosystems Based on the BERT-BiGRU-Att-CRF Model
by Danfeng Zhao, Xiaolian Chen and Yan Chen
Appl. Sci. 2024, 14(13), 5743; https://doi.org/10.3390/app14135743 - 1 Jul 2024
Viewed by 580
Abstract
In addressing the challenges of non-standardization and limited annotation resources in Chinese marine domain texts, particularly with complex entities like long and nested entities in coral reef ecosystem-related texts, existing Named Entity Recognition (NER) methods often fail to capture deep semantic features, leading [...] Read more.
In addressing the challenges of non-standardization and limited annotation resources in Chinese marine domain texts, particularly with complex entities like long and nested entities in coral reef ecosystem-related texts, existing Named Entity Recognition (NER) methods often fail to capture deep semantic features, leading to inefficiencies and inaccuracies. This study introduces a deep learning model that integrates Bidirectional Encoder Representations from Transformers (BERT), Bidirectional Gated Recurrent Units (BiGRU), and Conditional Random Fields (CRF), enhanced by an attention mechanism, to improve the recognition of complex entity structures. The model utilizes BERT to capture context-relevant character vectors, employs BiGRU to extract global semantic features, incorporates an attention mechanism to focus on key information, and uses CRF to produce optimized label sequences. We constructed a specialized coral reef ecosystem corpus to evaluate the model’s performance through a series of experiments. The results demonstrated that our model achieved an F1 score of 86.54%, significantly outperforming existing methods. The contributions of this research are threefold: (1) We designed an efficient named entity recognition framework for marine domain texts, improving the recognition of long and nested entities. (2) By introducing the attention mechanism, we enhanced the model’s ability to recognize complex entity structures in coral reef ecosystem texts. (3) This work offers new tools and perspectives for marine domain knowledge graph construction and study, laying a foundation for future research. These advancements propel the development of marine domain text analysis technology and provide valuable references for related research fields. Full article
(This article belongs to the Special Issue Environmental Monitoring and Analysis for Hydrology)
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23 pages, 9585 KiB  
Article
An Interpretable Deep Learning Approach for Detecting Marine Heatwaves Patterns
by Qi He, Zihang Zhu, Danfeng Zhao, Wei Song and Dongmei Huang
Appl. Sci. 2024, 14(2), 601; https://doi.org/10.3390/app14020601 - 10 Jan 2024
Cited by 1 | Viewed by 1366
Abstract
Marine heatwaves (MHWs) refer to a phenomenon where the sea surface temperature is significantly higher than the historical average for that region over a period, which is typically a result of the combined effects of climate change and local meteorological conditions, thereby potentially [...] Read more.
Marine heatwaves (MHWs) refer to a phenomenon where the sea surface temperature is significantly higher than the historical average for that region over a period, which is typically a result of the combined effects of climate change and local meteorological conditions, thereby potentially leading to alterations in marine ecosystems and an increased incidence of extreme weather events. MHWs have significant impacts on the marine environment, ecosystems, and economic livelihoods. In recent years, global warming has intensified MHWs, and research on MHWs has rapidly developed into an important research frontier. With the development of deep learning models, they have demonstrated remarkable performance in predicting sea surface temperature, which is instrumental in identifying and anticipating marine heatwaves (MHWs). However, the complexity of deep learning models makes it difficult for users to understand how the models make predictions, posing a challenge for scientists and decision-makers who rely on interpretable results to manage the risks associated with MHWs. In this study, we propose an interpretable model for discovering MHWs. We first input variables that are relevant to the occurrence of MHWs into an LSTM model and use a posteriori explanation method called Expected Gradients to represent the degree to which different variables affect the prediction results. Additionally, we decompose the LSTM model to examine the information flow within the model. Our method can be used to understand which features the deep learning model focuses on and how these features affect the model’s predictions. From the experimental results, this study provides a new perspective for understanding the causes of MHWs and demonstrates the prospect of future artificial intelligence-assisted scientific discovery. Full article
(This article belongs to the Special Issue Environmental Monitoring and Analysis for Hydrology)
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14 pages, 16564 KiB  
Article
Deep Learning-Based Classification of High-Resolution Satellite Images for Mangrove Mapping
by Yidi Wei, Yongcun Cheng, Xiaobin Yin, Qing Xu, Jiangchen Ke and Xueding Li
Appl. Sci. 2023, 13(14), 8526; https://doi.org/10.3390/app13148526 - 24 Jul 2023
Cited by 5 | Viewed by 1860
Abstract
Detailed information about mangroves is crucial for ecological and environmental protection and sustainable development. It is difficult to capture small patches of mangroves from satellite images with relatively low to medium resolution. In this study, high-resolution (0.8–2 m) images from Chinese GaoFen (GF) [...] Read more.
Detailed information about mangroves is crucial for ecological and environmental protection and sustainable development. It is difficult to capture small patches of mangroves from satellite images with relatively low to medium resolution. In this study, high-resolution (0.8–2 m) images from Chinese GaoFen (GF) and ZiYuan (ZY) series satellites were used to map the distribution of mangroves in coastal areas of Guangdong Province, China. A deep-learning network, U2-Net, with attention gates was applied to extract multi-scale information of mangroves from satellite images. The results showed that the attention U2-Net model performed well on mangrove classification. The overall accuracy, precision, and F1-score values were 96.5%, 92.0%, and 91.5%, respectively, which were higher than those obtained from other machine-learning methods such as Random Forest or U-Net. Based on the high-resolution mangrove maps generated from long satellite image time series, we also investigated the spatiotemporal evolution of the mangrove forest in Shuidong Bay. The results can provide crucial information for government administrators, scientists, and other stakeholders to monitor the dynamic changes in mangroves. Full article
(This article belongs to the Special Issue Environmental Monitoring and Analysis for Hydrology)
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15 pages, 6937 KiB  
Article
Study on the Impact of the Coastline Changes on Hydrodynamics in Xiangshan Bay
by Yikai Xu, Yiting Wang, Song Hu, Yuanli Zhu, Juncheng Zuo and Jiangning Zeng
Appl. Sci. 2023, 13(14), 8071; https://doi.org/10.3390/app13148071 - 11 Jul 2023
Cited by 1 | Viewed by 1021
Abstract
Coastline changes have significant impacts on coastal hydrodynamics. Xiangshan Bay is a semi-closed and long-narrow bay located in Zhejiang Province, China. Its coastline has changed dramatically in recent decades; however, the variations in the small-scale hydrodynamics in the changed coastline areas have not [...] Read more.
Coastline changes have significant impacts on coastal hydrodynamics. Xiangshan Bay is a semi-closed and long-narrow bay located in Zhejiang Province, China. Its coastline has changed dramatically in recent decades; however, the variations in the small-scale hydrodynamics in the changed coastline areas have not been carefully studied. This study uses the Finite-Volume Community Ocean Model (FVCOM) to design a set of control experiments and five sets of compared experiments targeting the areas with significant coastline changes in Xiangshan Bay over the past 21 years. It was found that the coastline changes at the mouth of the bay, such as areas near Meishan Island and Dasong, have a significant impact on both residual currents and tidal currents, changing the amplitudes and phase distributions of the tides. Coastline changes in the inner bay have lesser impacts on hydrodynamics, mainly affecting the small-scale areas in the vicinity. Full article
(This article belongs to the Special Issue Environmental Monitoring and Analysis for Hydrology)
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