Susceptibility to Translational Landslides in Ecuador Caused by Changes in Electrical Permittivity of Andepts Soils Using Software-Defined Radar for Detection

Landslides are widespread and global geological disasters, affecting millions of people and causing numerous deaths each year. Despite technological advances, it is still difficult to accurately prevent landslides. Due to its geography and climatic conditions, Ecuador has been significantly affected by landslides, and the city of Penipe remains one of the most affected. For this reason, a low-cost SDRadar system was designed to detect translational landslide risk levels by measuring the electrical permittivity of Andepts subtype soils. Controlled laboratory tests were performed with soil samples to relate permittivity values to landslide risk levels, and subsequently field tests were carried out in Penipe to determine the efficiency of the methodology. The results showed that moderate humidity is important for soil compaction, regardless of the degree of sloping. However, with permittivity values lower than 1.5 or higher than 20, the risk of landslides is very high on slopes greater than 45°. These results were compared with records of the serious landslides that occurred in June 2024 in Ecuador, in which rainfall intensity values similar to those obtained in this study were recorded, suggesting that this system can prevent future disasters.

Authors

Institutions

Publication Details

Journal
Earth
Published
2024-10-18
DOI
https://doi.org/10.3390/earth5040035
Citations
1
Primary Topic
Landslides and related hazards
Type
article
Field-Weighted Citation Impact
0.71
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Susceptibility to Translational Landslides in Ecuador Caused by Changes in Electrical Permittivity of Andepts Soils Using Software-Defined Radar for Detection

Diego Veloz-Cherrez, M. N. Navarro Ojeda, Johanna Del Pozo
1 citations
Earth
Landslides and related hazards
0.71
article

Susceptibility to Translational Landslides in Ecuador Caused by Changes in Electrical Permittivity of Andepts Soils Using Software-Defined Radar for Detection

Diego Veloz-Cherrez, M. N. Navarro Ojeda, Johanna Del Pozo
article en
1 citations

Abstract

Landslides are widespread and global geological disasters, affecting millions of people and causing numerous deaths each year. Despite technological advances, it is still difficult to accurately prevent landslides. Due to its geography and climatic conditions, Ecuador has been significantly affected by landslides, and the city of Penipe remains one of the most affected. For this reason, a low-cost SDRadar system was designed to detect translational landslide risk levels by measuring the electrical permittivity of Andepts subtype soils. Controlled laboratory tests were performed with soil samples to relate permittivity values to landslide risk levels, and subsequently field tests were carried out in Penipe to determine the efficiency of the methodology. The results showed that moderate humidity is important for soil compaction, regardless of the degree of sloping. However, with permittivity values lower than 1.5 or higher than 20, the risk of landslides is very high on slopes greater than 45°. These results were compared with records of the serious landslides that occurred in June 2024 in Ecuador, in which rainfall intensity values similar to those obtained in this study were recorded, suggesting that this system can prevent future disasters.

EarthVol. 5(4)
Escuela Superior Politécnica del Chimborazo (EC)
Climate action
Openalex Percentile: Top 21%
Landslides and related hazards
0.71
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.