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Environmental Monitoring and Analysis for Hydrology

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

Deadline for manuscript submissions: closed (20 April 2025) | Viewed by 12436

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Special Issue Editors

Prof. Dr. Wei Song

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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

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Guest Editor

Faculty of Computer Science, Free University of Bozen-Bolzano, 39100 Bolzano, Italy
Interests: data analsis; machine learning; smart systems
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Dr. Qi He

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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

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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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Published Papers (7 papers)

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Research

24 pages, 13248 KiB

Open AccessArticle

GIS-Based Flood Assessment Using Hydraulic Modeling and Open Source Data: An Example of Application

by Loredana Copăcean, Eugen Teodor Man, Luminiţa L. Cojocariu, Cosmin Alin Popescu, Clara-Beatrice Vîlceanu, Robert Beilicci, Alina Creţan, Mihai Valentin Herbei, Ovidiu Ştefan Cuzic and Sorin Herban

Appl. Sci. 2025, 15(5), 2520; https://doi.org/10.3390/app15052520 - 26 Feb 2025

Viewed by 988

Abstract

The study explores the impact of floods, phenomena amplified by climate change and human activities, on the natural and anthropogenic environment, focusing on the analysis of a section of the Cigher River in the Crișul Alb basin in western Romania. The research aims to identify areas vulnerable to flooding under different discharge scenarios, assess the impact on agricultural lands, and propose a reproducible methodology based on the integration of GIS technologies, hydraulic modeling in HEC-RAS, and the use of LiDAR data. The methodology includes hydrological analysis, processing of the Digital Elevation Model (DEM), delineation of geometries, hydraulic simulation for four discharge scenarios (S1–S4), and evaluation of the flood impact on agricultural and non-agricultural lands. Evaluated parameters, such as water velocity and flow section areas, highlighted an increased flood risk under maximum discharge conditions. The results show that scenario S4, with a discharge of 60 m³/s, causes extensive flooding, affecting 871 hectares of land with various uses. The conclusions emphasize the importance of using modern technologies for risk management, protecting vulnerable areas, and reducing economic and ecological losses. The proposed methodology is also applicable to other river basins, representing a useful model for developing sustainable strategies for flood prevention and management. Full article

(This article belongs to the Special Issue Environmental Monitoring and Analysis for Hydrology)

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20 pages, 11833 KiB

Open AccessArticle

Coupling and Comparison of Physical Mechanism and Machine Learning Models for Water Level Simulation in Plain River Network Area

by Xiaoqing Gao, Yunzhu Liu, Cheng Gao, Dandan Qing, Qian Wang and Yulong Cai

Appl. Sci. 2024, 14(24), 12008; https://doi.org/10.3390/app142412008 - 22 Dec 2024

Viewed by 735

Abstract

In this study, the JiaoGang Basin in the Yangtze River Delta plains of the river network area was the research object. A basin water level simulation model was constructed based on the physical mechanism model and Mike software, and the parameters were calibrated and validated. Based on the dataset produced by the physical model, three types of ML models, Support Vector Machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT), were constructed, trained, validated, and compared with the physical model. The results showed that the physical mechanism model met the water level simulation accuracy requirements at most stations. In the training and validation periods, the RF water level simulation and GBDT water level simulation models had root mean square errors (RMSEs) of all stations less than 0.25 and the Nash–Sutcliffe coefficient (NSE) of all stations was greater than 0.7. The physical mechanism model and ML water level simulation models can simulate the water level in the JiaoGang Basin better. The RF and GBDT models considerably outperform the physical mechanism model in terms of the peak simulation errors and peak present time errors, and the fluctuations of the ML water level simulation models (RMSE and NSE) are minor compared to those of the physical mechanism model. Full article

(This article belongs to the Special Issue Environmental Monitoring and Analysis for Hydrology)

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17 pages, 5968 KiB

Open AccessArticle

Status and Migration Activity of Lead, Cobalt and Nickel in Water and in Bottom Sediments of Lake Markakol, Kazakhstan

by Laura Ismukhanova, Azamat Madibekov, Christian Opp, Askhat Zhadi, Botakoz Sultanbekova and Serik Zhumatayev

Appl. Sci. 2024, 14(17), 7487; https://doi.org/10.3390/app14177487 - 24 Aug 2024

Viewed by 890

Abstract

Lake Markakol is located in a metal-rich mountain area of Kazakhstan. Metal input into the lake water and in the bottom sediments can be expected. Lead, cobalt and nickel monitoring in both near-surface and deep-water layers and in bottom sediments was carried out using flame atomic absorption spectrometric analyses. Lead contamination of surface water ranging from 2.6 to 6.8 µg/L occurs in all water samples with the exception of the surface water layer. In the deep-water section concentrations reach up to 13.0–16.2 µg/L. Cobalt concentrations range from 36.8 to 67.5 µg/L in the surface layer and from 25.5 to 69.2 µg/L in the deep-water layer. High values of nickel were found in the surface and bottom layers of the water, ranging from 13.5 to 49.0 and 17.2 to 49.0 µg/L, respectively. High concentrations of lead, cobalt and nickel were identified in all samples of the bottom sediments. The lead content in bottom sediments reaches 11.3, cobalt reaches 10.3–18.0 and nickel reaches 15.0 mg kg⁻¹. The results and their assessment can serve as a basis for future monitoring and measures to reduce pollution, restore the lake ecosystem and ensure the safety of fishery products for humans. Full article

(This article belongs to the Special Issue Environmental Monitoring and Analysis for Hydrology)

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17 pages, 2543 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 1396

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 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

Open AccessArticle

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 2 | Viewed by 2735

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 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

Open AccessArticle

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 8 | Viewed by 2894

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) 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

Open AccessArticle

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 2 | Viewed by 1406

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 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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