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Geosciences
Special Issues
Monitoring and Modeling Gully Erosion
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Monitoring and Modeling Gully Erosion

A special issue of Geosciences (ISSN 2076-3263).

Deadline for manuscript submissions: closed (25 September 2022) | Viewed by 3861

Special Issue Editors

Dr. Ingrid Luffman

E-Mail Website
Guest Editor
Department of Geosciences, East Tennessee State University, Johnson City, TN 37614, USA
Interests: gully erosion; water quality; watershed processes; geostatistical analyses; medical geography; karst hydrology
Dr. Arpita Nandi

E-Mail Website
Guest Editor
Department of Geosciences, East Tennessee State University, Johnson City, TN 37614, USA
Interests: gully erosion; soil mechanics; rock mechanics; slope stability hazard and risk; engineering geology; geospatial analysis

Special Issue Information

Dear Colleagues,

Gully erosion occurs when water runoff concentrates into channels and begins to erode unprotected land. This is a global problem resulting in loss of arable land, threats to infrastructure, increased runoff, and stream sedimentation, and is influenced by climate, soil, land use, and geomorphology. Its impacts are ecological, economic, and environmental. 

Multiple approaches exist to assess gully erosion, including monitoring sediment yield, investigating driving processes, modeling the storage and transport of sediment, and assessing the environmental impacts of erosion. With recent developments in remote sensing, geospatial, and ground-based technology, further research efforts in proactive gully erosion management are well justified. Additionally, an understanding of state-of-the-art practice on effective gully erosion control measures will allow practitioners to choose the best land-use management practices to address this challenging problem.  
In this Special Issue we seek to gather contributions that address the monitoring and modeling of gully erosion, including methods of gully identification and development, sediment yield and transport, environmental impacts of gully erosion, and gully erosion control measures.   

Topics of interest include (but are not limited to):

  • Remote sensing structure from motion models, LiDAR, or similar.
  • Application of GIS in gully erosion monitoring and modeling.
  • Statistical modeling of gully erosion, including spatial and/or temporal models.
  • Field and laboratory experiments to monitor gully erosion.
  • Quantifying gully erosion and/or sediment yield.
  • Sediment storage and transport in gullies.
  • Model development and/or evaluation.
  • Model calibration and validation.
  • Quantifying uncertainty.
  • Impacts of gully erosion.
  • Gully erosion control.
  • Climate change influences on gully erosion.

Dr. Ingrid Luffman
Dr. Arpita Nandi
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. Geosciences is an international peer-reviewed open access monthly 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 1800 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

  • gully erosion model
  • monitoring gully erosion
  • gully erosion impacts
  • uncertainty
  • field methods
  • sediment storage and transport
  • gully erosion control

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Published Papers (1 paper)

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Review

23 pages, 1017 KiB  
Review
Machine Learning Techniques for Gully Erosion Susceptibility Mapping: A Review
by Hamid Mohebzadeh, Asim Biswas, Ramesh Rudra and Prasad Daggupati
Geosciences 2022, 12(12), 429; https://doi.org/10.3390/geosciences12120429 - 22 Nov 2022
Cited by 11 | Viewed by 3290
Abstract
Gully erosion susceptibility mapping (GESM) through predicting the spatial distribution of areas prone to gully erosion is required to plan gully erosion control strategies relevant to soil conservation. Recently, machine learning (ML) models have received increasing attention for GESM due to their vast [...] Read more.
Gully erosion susceptibility mapping (GESM) through predicting the spatial distribution of areas prone to gully erosion is required to plan gully erosion control strategies relevant to soil conservation. Recently, machine learning (ML) models have received increasing attention for GESM due to their vast capabilities. In this context, this paper sought to review the modeling procedure of GESM using ML models, including the required datasets and model development and validation. The results showed that elevation, slope, plan curvature, rainfall and land use/cover were the most important factors for GESM. It is also concluded that although ML models predict the locations of zones prone to gullying reasonably well, performance ranking of such methods is difficult because they yield different results based on the quality of the training dataset, the structure of the models, and the performance indicators. Among the ML techniques, random forest (RF) and support vector machine (SVM) are the most widely used models for GESM, which show promising results. Overall, to improve the prediction performance of ML models, the use of data-mining techniques to improve the quality of the dataset and of an ensemble estimation approach is recommended. Furthermore, evaluation of ML models for the prediction of other types of gully erosion, such as rill–interill and ephemeral gully should be the subject of more studies in the future. The employment of a combination of topographic indices and ML models is recommended for the accurate extraction of gully trajectories that are the main input of some process-based models. Full article
(This article belongs to the Special Issue Monitoring and Modeling Gully Erosion)
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