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 3258

Special Issue Editors

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
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
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

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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

Published Papers (3 papers)

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Research

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
Viewed by 658
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 3 | Viewed by 1275
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 738
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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