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Water
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Applications of Remote Sensing and Machine Learning in Water Resources...
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Applications of Remote Sensing and Machine Learning in Water Resources Management

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: 25 October 2024 | Viewed by 1979

Special Issue Editors

Dr. Won Seok Jang

E-Mail Website
Guest Editor
Department of Ecological Landscape Architecture Design, Kangwon National University, Chuncheon, Republic of Korea
Interests: water resources management; computational ecohydrology; soil and water conservation; environmental impact assessment; machine learning applications
Dr. Jiwan Lee

E-Mail Website
Guest Editor
Han River Flood Control Office, Seoul, Republic of Korea
Interests: water resources management; drought analysis; remote sensing; hydrology; sustainable agriculture; soil moisture
Dr. Seungsoo Lee

E-Mail Website
Guest Editor
Korea Environment Institute (KEI), Sejong, Republic of Korea
Interests: hydrology; hydraulics; flooding; urban inundation; machine learning

Special Issue Information

Dear Colleagues,

I cordially invite you to contribute to the Special Issue in the open access journal Water, entitled “Applications of Remote Sensing and Machine Learning in Water Resources Management”.

Water resources management addresses complex issues in the control of such resources available on Earth. Countries establish systems and regulations that oversee water for a variety of uses. When water resources are managed well, communities and governments benefit; if they are not, serious global consequences ensue. Adequate water resources management requires accurate assessment and prediction using advanced and effective techniques (e.g., machine learning, remote sensing, etc.). Thus, this Special Issue offers researchers the opportunity to share their achievements in the following topics using remote sensing and machine learning:

  • Surface and groundwater interactions for watershed management;
  • Advancement of watershed management modelling;
  • Quantitative analysis of floods or droughts;
  • Computational advances in water resources modelling;
  • Analysis of water use and water availability;
  • Sustainable water resources for agriculture;
  • Potential and limitations of natural-based solutions (NBS) to water resources management;
  • The impact of climatic changes on water resources management;
  • Retrospective urban inundation modelling;
  • Advancement in urban inundation forecasting technique;
  • Validation of satellite products related to water resources (e.g., soil moisture and vegetation indices);
  • Development of innovative numerical techniques for integrating machine learning with satellite-based remote sensing data to enhance drought monitoring and forecasting;
  • Other topics related to water resources management.

Dr. Won Seok Jang
Dr. Jiwan Lee
Dr. Seungsoo Lee
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. 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

  • climate change
  • droughts
  • evapotranspiration
  • floods
  • forecasting
  • hydrological drought indices
  • machine learning
  • modelling
  • real-time forecasting
  • remote sensing
  • soil moisture
  • sustainable water resources
  • urban inundation
  • watershed management

Published Papers (2 papers)

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Research

29 pages, 16471 KiB  
Article
Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh
by Polina Lemenkova
Water 2024, 16(8), 1141; https://doi.org/10.3390/w16081141 - 17 Apr 2024
Viewed by 925
Abstract
Mapping spatial data is essential for the monitoring of flooded areas, prognosis of hazards and prevention of flood risks. The Ganges River Delta, Bangladesh, is the world’s largest river delta and is prone to floods that impact social–natural systems through losses of lives [...] Read more.
Mapping spatial data is essential for the monitoring of flooded areas, prognosis of hazards and prevention of flood risks. The Ganges River Delta, Bangladesh, is the world’s largest river delta and is prone to floods that impact social–natural systems through losses of lives and damage to infrastructure and landscapes. Millions of people living in this region are vulnerable to repetitive floods due to exposure, high susceptibility and low resilience. Cumulative effects of the monsoon climate, repetitive rainfall, tropical cyclones and the hydrogeologic setting of the Ganges River Delta increase probability of floods. While engineering methods of flood mitigation include practical solutions (technical construction of dams, bridges and hydraulic drains), regulation of traffic and land planning support systems, geoinformation methods rely on the modelling of remote sensing (RS) data to evaluate the dynamics of flood hazards. Geoinformation is indispensable for mapping catchments of flooded areas and visualization of affected regions in real-time flood monitoring, in addition to implementing and developing emergency plans and vulnerability assessment through warning systems supported by RS data. In this regard, this study used RS data to monitor the southern segment of the Ganges River Delta. Multispectral Landsat 8-9 OLI/TIRS satellite images were evaluated in flood (March) and post-flood (November) periods for analysis of flood extent and landscape changes. Deep Learning (DL) algorithms of GRASS GIS and modules of qualitative and quantitative analysis were used as advanced methods of satellite image processing. The results constitute a series of maps based on the classified images for the monitoring of floods in the Ganges River Delta. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and Machine Learning in Water Resources Management)
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15 pages, 13206 KiB  
Article
Cause Analysis of Salinity Intrusion by Environmental Changes Considering Water Intake and Sand Mining on Seomjin River Estuary Using Model for Maintaining Corbicula Habitats
by Chunggil Jung, Gayeong Lee and Jongyoon Park
Water 2024, 16(7), 1035; https://doi.org/10.3390/w16071035 - 3 Apr 2024
Viewed by 747
Abstract
Anthropogenic development can strongly influence natural river processes, leading to environmental changes that negatively affect important habitats and biodiversity and consequently reduce economically important natural resources. This study investigated the effects of salinity intrusion on the habitat of the clam Corbicula japonica in [...] Read more.
Anthropogenic development can strongly influence natural river processes, leading to environmental changes that negatively affect important habitats and biodiversity and consequently reduce economically important natural resources. This study investigated the effects of salinity intrusion on the habitat of the clam Corbicula japonica in the Seomjin River estuarine zone. We employed the Environmental Fluid Dynamics Code (EFDC) model, which incorporates topographic data and hydrological changes, to simulate salinity. Two salinity measurement facilities were installed in Seomjin River estuarine and operated to optimize the EFDC model. The results show that reduced flow rates due to intake have a negligible impact on the increased salinity. Maintaining optimal salinity (15–20 psu) during neap tides at the Seomjin River Bridge requires constant high flow rates, which poses significant challenges. Saltwater stratification is identified as the primary cause of pronounced salinity stratification, particularly during neap tides. Addressing this issue through river discharge and intake facility operation is challenging. Structural measures, including riverbed restoration and underwater barriers, are recommended to improve resistance to seawater intrusion. Future research should aim to develop scenarios to reduce salinity, quantify the reduction efficiency, and propose region-specific measures. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and Machine Learning in Water Resources Management)
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