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Water
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Application of Remote Sensing and GIS in Prediction Hydrogeological Hazards
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Application of Remote Sensing and GIS in Prediction Hydrogeological Hazards

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

Deadline for manuscript submissions: 20 May 2026 | Viewed by 2865

Special Issue Editors

Prof. Dr. Abolghasem Sadeghi-Niaraki

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, XR Research Center, Sejong University, Seoul 03063, Republic of Korea
Interests: artificial intelligence (GeoAI); extended reality (XR); geographic information systems (GISs); HCI; culture technology; metaverse
Special Issues, Collections and Topics in MDPI journals
Dr. Seyed Vahid Razavi-Termeh

E-Mail Website
Guest Editor
Department of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul 05006, Republic of Korea
Interests: geospatial artificial intelligence (GeoAI); geosciences, natural hazard; machine/deep learning algorithms; geographic information system (GIS); remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Hydrological hazards, including floods and droughts, pose significant threats to ecosystems, infrastructure, and human well-being. These challenges are exacerbated by climate change, urbanization, and land-use alterations, necessitating innovative strategies for their prediction and management. Remote sensing technologies, when combined with Geographic Information Systems (GISs) and spatial modeling techniques, offer robust tools for assessing hydrological risks and understanding processes at multiple scales. These approaches enable real-time monitoring, risk mapping, and scenario-based forecasting, empowering researchers and policymakers to make informed decisions for sustainable water resource management.

This Special Issue will highlight recent advancements in the application of remote sensing, GISs, and spatial modeling to hydrological hazard prediction and management. By fostering interdisciplinary collaboration, it will contribute to the development of innovative methodologies and practical tools to address these pressing challenges.

Topics of interest include, but are not limited to, the following:

  • Advances in remote sensing technologies for hydrological hazard monitoring;
  • Integration of multi-source remote sensing data for hazard prediction;
  • Machine learning and AI applications in remote sensing for hydrological modeling;
  • Real-time monitoring and early warning systems for floods and droughts;
  • GIS-based spatial modeling for hazard prediction and vulnerability assessment;
  • Applications of spatiotemporal modeling in hydrological hazard prediction;
  • Evaluation of the reliability and accuracy of geospatial data in hazard prediction.

We look forward to receiving your valuable contributions to this Special Issue.

Prof. Dr. Abolghasem Sadeghi-Niaraki
Dr. Seyed Vahid Razavi-Termeh
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 250 words) can be sent to the Editorial Office for assessment.

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

  • hydrological hazards
  • spatial modeling
  • geographic information systems (GISs)
  • remote sensing
  • flood prediction
  • drought monitoring
  • geospatial artificial intelligence (GeoAI)
  • climate change impacts

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Published Papers (2 papers)

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24 pages, 13424 KB  
Article
Utilizing Deep Learning and Object-Based Image Analysis to Search for Low-Head Dams in Indiana, USA
by Brian M. Crookston and Caitlin R. Arnold
Water 2025, 17(6), 876; https://doi.org/10.3390/w17060876 - 18 Mar 2025
Viewed by 1794
Abstract
Although low-head dams in the USA provide water supply, irrigation, and recreation opportunities, many are unknown by regulators. Unfortunately, hundreds of drownings occur each decade at these dams from an entrapment current that can form immediately downstream. To explore the ability of deep [...] Read more.
Although low-head dams in the USA provide water supply, irrigation, and recreation opportunities, many are unknown by regulators. Unfortunately, hundreds of drownings occur each decade at these dams from an entrapment current that can form immediately downstream. To explore the ability of deep learning to scan large areas of terrain to identify the locations of low-head dams, ArcGIS Pro and embedded deep learning models for object-based image analysis were investigated. The State of Indiana low-head dam dataset was selected for model training and validation. Aerial imagery (leaf-off conditions) captured from 2016 to 2018 for the nearly 94,000 km2 area had a minimum resolution of 304.8 mm. A new Python code was developed that automated the generation of training images and searching was limited to 100 m wide river corridors. Due to bank vegetation, all low-head dams were assigned a visibility score to aid in training and performance analysis. A total of 19 backbone models were considered with single shot detection and options for RetinaNet, Faster R-CNN, and batch normalization. Additional identification classes were incorporated to overcome identification of visually similar objects. After four training iterations, the final trained model was a ResNet RetinaNet backbone model featuring 101 layers with an 83% recall rate for dams with high visibility and a 17% recall rate for those with moderate visibility. Full article
(This article belongs to the Special Issue Application of Remote Sensing and GIS in Prediction Hydrogeological Hazards)
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24 pages, 6422 KB  
Technical Note
Susceptibility Mapping of Glacial Lake Outburst Debris Flows Based on System Failure Model
by Wei Qian, Juan Du, Bo Chai and Yu Wang
Water 2026, 18(6), 651; https://doi.org/10.3390/w18060651 - 10 Mar 2026
Viewed by 419
Abstract
Global climate warming has increased the risk of glacial lake outburst debris flows (GLODFs) in high mountain regions. It is characterized by frequent and clustered occurrences, particularly in the Himalayan region, and represents an inescapable challenge for high mountain areas in the future. [...] Read more.
Global climate warming has increased the risk of glacial lake outburst debris flows (GLODFs) in high mountain regions. It is characterized by frequent and clustered occurrences, particularly in the Himalayan region, and represents an inescapable challenge for high mountain areas in the future. GLODF susceptibility assessment is critical to risk mitigation but remains a challenge owing to its complex triggering mechanisms and watershed structure. GLODF is a complex system failure process, including the failure probabilities of multiple glacial lakes in a watershed, the complex flow path of flood, the transition probability from flood to debris flow, and the overlapping of debris flows formed in different branches in the watershed. Therefore, multiple trigger factors, hazard sources and flow paths should be considered in the assessment of susceptibility to GLODFs. In this study, a systematic approach and mapping for GLODF susceptibility assessment are proposed based on the theory of system failure analysis. The main steps include: (1) identification and classification of the potential hazard sources in the target watershed; (2) arrangement of the flow path and abstraction of the key-node diagram; (3) establishment of the system failure structure of a GLODF; and (4) predisposing factor analysis and susceptibility assessment. Moreover, the predisposing indexes of GLODF susceptibility assessment are proposed, combining the main factors affecting both glacial lake outbursts and subsequent debris flows. The proposed model was applied in the Congduipu River basin, Nyalam, Tibet, China, which has more than 6 glacial lakes and 11 gullies, with an area of 366 km2, and encountered more than four GLODFs in recent years. The results show that there are one very high-susceptibility glacial lake, two high-susceptibility glacial lakes, and gullies that are in series with high-susceptibility glacial lakes that are mostly medium–highly susceptible to glacial outbursts. The results were verified by historical records and field investigations in the Congduipu River basin. This method is applicable to quickly evaluate the susceptibility to GLODFs at the watershed and regional scales with multiple glacial lakes and gullies. Full article
(This article belongs to the Special Issue Application of Remote Sensing and GIS in Prediction Hydrogeological Hazards)
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