InSAR-BASED LANDSLIDE MOVEMENT MODELS: A CASE STUDY OF JIZAN PROVINCE, SAUDI ARABIA

Roman Shults*, Esubalew Adem, Md Masudur Rahman

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Landslides are natural hazards that lead to significant damage and human victims yearly. The territory of the Kingdom of Saudi Arabia is relatively safe and does not contain large landslide-prone regions. However, the west coast of the Arabian Peninsula is susceptible to landslide activity. Therefore, monitoring these regions is badly needed. The best way to observe the areas susceptible to landslide activity is the application of remote sensing technologies, particularly InSAR. The presented paper deals with an analysis of the monitoring of InSAR in Jizan province. The monitoring data span observation epochs 2020-2023. Since the monitoring region lacks reliable reflecting surfaces, the displacements were obtained using the SBAS processing algorithm. For analysis, the GMDH algorithm was applied. Meteorological parameters and landslide susceptibility index were used as independent variables. Different GMDH processing strategies were tested, and the optimal one was selected. GMDH algorithms demonstrated high efficiency and flexibility for analyzing such complex data.

Original languageEnglish
Title of host publication2024 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350390346
DOIs
StatePublished - 2024
Event2024 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2024 - Goa, India
Duration: 2 Dec 20245 Dec 2024

Publication series

Name2024 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2024

Conference

Conference2024 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2024
Country/TerritoryIndia
CityGoa
Period2/12/245/12/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

  • GMDH
  • InSAR
  • machine learning
  • prediction model
  • SBAS

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Earth and Planetary Sciences (miscellaneous)
  • Earth-Surface Processes
  • Space and Planetary Science
  • Aerospace Engineering
  • Instrumentation

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