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Special Issue "Prediction of Ground Displacement and Landslide Susceptibility Based on Past Relevant Data"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation for Emergency Management".

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 2365

Special Issue Editor

Stamatopoulos and Associates Ltd., Hellenic Open University, 5 Isavron Str., 11471 Athens, Greece
Interests: soil mechnics; soil stabilty; earthquake engineering; numerical modeling; landslides; ground movement; ground improvement

Special Issue Information

Dear Colleagues,

Landslides involve excessive movement of natural and man-made slopes, usually along a slip surface, often triggered by prolonged rainfall or earthquakes. They are one of the most destructive hazards in the world. Furthermore, in many arid planar regions of the world, ground subsidence induced by the lowering of the water table line due to pumping has recently caused damage to houses and other overlying structures. Landslides occurrences and ground subsidence are expected to increase in the near future as a result of global warming, where rainfall patterns are changing.

The significance of the hazard describe above generates the need to study and propose prediction methods both in a regional and global scale. Recently, rigorous machine learning methods have been applied in regional landslide susceptibility mapping in terms of landslide inventory maps and relevant factors affecting ground instability. In addition, recently new technologies, such as space interferometry have been developed which provide cost-effective measurements of past ground displacement data. Furthermore, past ground subsidence data has recently been analyzed in order to provide in-situ measurement of the underlying soil consolidation process, needed for the prediction of future ground subsidence.

Yet, application of such modern methods, technologies and analyses in ground displacement and landslide susceptibility are still in a preliminary investigative stage. This special issue of Remote Sensing invites papers in the interesting and timely topic of “Prediction of ground displacement and landslide susceptibility based on past relevent data”.

Dr. Constantine Stamatopoulos
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at 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. Remote Sensing 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 2700 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.


  • landslides
  • slope instability
  • ground subsidence
  • measurement of ground displacement
  • prediction of ground displacement
  • rainfall
  • earthquakes
  • pumping

Published Papers (1 paper)

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Application of Machine Learning in Forecasting the Impact of Mining Deformation: A Case Study of Underground Copper Mines in Poland
Remote Sens. 2022, 14(19), 4755; - 23 Sep 2022
Cited by 2 | Viewed by 1494
Open access to SAR data from the Sentinel 1 missions allows analyses of long-term ground surface changes. The current data-acquisition frequency of 12 days facilitates the continuous monitoring of phenomena such as volcanic and tectonic activity or mining-related deformations. SAR data are increasingly [...] Read more.
Open access to SAR data from the Sentinel 1 missions allows analyses of long-term ground surface changes. The current data-acquisition frequency of 12 days facilitates the continuous monitoring of phenomena such as volcanic and tectonic activity or mining-related deformations. SAR data are increasingly also used as input data in forecasting phenomena on the basis of machine learning. This article presents the possibility of using selected machine learning algorithms in forecasting the influence of underground mining activity on the ground surface. The study was performed for a mining protective area with a surface of over 500 km2 and located in western Poland. The ground surface displacements were calculated for the period from November 2014 to July 2021, with the use of the Small Baseline Subset (SBAS) method. The forecasts were performed for a total of 22 identified subsidence troughs. Each of the troughs was provided with two profiles, with a total of more than 10,000 identified points. The selected algorithms served to prepare 180-day displacement forecasts. The best results (significantly better than the baseline) were obtained with the ARIMA and Holt models. Linear models also provided better results than the baseline and their performance was very good at up to 2 months forecasting. Tree-based models including their sophisticated ensemble versions: bagging (Random Forest, Extra Trees) and boosting (XGBoost, LightGBM, CatBoost, Gradient Boosting, Hist Gradient Boosting) cannot be used for this type of predictions since Decision Trees are not able to extrapolate and thus are not a valid stand-alone tool for forecasting in this type of problems. A combination of satellite remote sensing data and machine learning facilitated both the simultaneous quasi-permanent monitoring of ground surface displacements and their forecasting in a relatively long time period. Full article
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