The selective causative factors on landslide susceptibility assessment: Case study Takengon, Aceh, Indonesia

Pamela,; Arifianti, Yukni; Imam, Sadisun A.; Kartiko, Rendy D.

doi:10.1063/1.5047374

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…Results of the WoE values that have been added are given a gradation of green-red, where red has a high WoE value, which indicates a high tendency for landslides. Comparison with related research in a different geological background, Pamela et al, (2018) in the research area of Takengon, Aceh concluded that the slope gradient is also the most dominant causative factor that affects the stability of a slope. Other parameters that most influenced landslides in their research area are aspects, peak ground acceleration, and elevation.…”

Section: Resultsmentioning

confidence: 90%

“…According to Ayalew et al (2005); in Che et al (2011), to determine the parameters that cause the formation of landslides using a statistical approach based on GIS, these parameters must meet the requirements including, can be operated, represent the whole area, not excessive and can be calculated. Pamela et al, (2018) use eight selective causative factors including slope degree, slope aspect, lithology, elevation, rainfall, distance to lineament, peak ground acceleration and flow direction.…”

Section: Introductionmentioning

confidence: 99%

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Landslide susceptibility analysis in Kabandungan District and Salak Geothermal Field, West Java

Misbahudin

2020

JGLITrop

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Landslide hazards can be caused by several factors such as lithology, land cover, rainfall, slope, curvature, aspect, distance from river and road. In this study, a landslide susceptibility mapping was carried out using a Geographical Information System (GIS) in Kabandungan District and Salak Geothermal Field, West Java. The data used consisted of an inventory of points and landslide areas totalling 247 using a visual collection of Google Earth imagery. The Weight of Evidence (WoE) model is used to select parameters that cause landslides and to produce landslide vulnerability maps. The results of modeling indicate a positive relationship between selective factors for the occurrence of landslides, with Area Under Curve value of 0.89359; 0.76395; 0.75277; 0.73280 and 0.69093 respectively. Landslide susceptibility maps are made by adding up the WoE values for all the most influential parameters. Higher total WoE value is indicating a higher probability of landslide. Landslide susceptibility maps can be used as an effort to prevent potential hazards or mitigate landslides. In addition, this map can also be used furtherly for spatial planning and engineering activities.

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Section: Resultsmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Landslide susceptibility analysis in Kabandungan District and Salak Geothermal Field, West Java

Misbahudin

2020

JGLITrop

View full text Add to dashboard Cite

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“…Several studies have shown different results on the factors that cause landslides. These studies showed slope as a significant factor (Okoli et al, 2023;Pamela et al, 2018), but other studies that also used slope did not show the most significant results among the factors used such as elevation (Ling et al, 2022;Pourghasemi et al, 2020); distance to road (Wang et al, 2019;Zhao et al, 2024); fault (Guo et al, 2021), landuse (Hemasinghe et al, 2018) and manymore.…”

Section: Landslide Susceptibility and Factorsmentioning

confidence: 96%

Optimizing Tourism Development Through Landslide Hazard Mapping in Raung Volcano

Bachri,

Shrestha,

Sumarmi

et al. 2024

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The series of volcanic activities of Mount Raung triggered primary and secondary hazards in the area around the volcano. Kalibaru watershed is one of the areas prone to landslides due to secondary hazards of eruption activity in the direction of west and northwest part of this region. This study aims to optimize tourism by mapping landslide hazard using Spatial Multi-Criteria Evaluation (SMCE) around Mount Raung. This research used 46 points of landslide data through remote sensing, field observation. Ten landslide triggering factors, namely TPI (Topographic Position Index), TWI (Topographic Wetness Index), SPI (Stream Power Index), slope, distance to river, rainfall, geology, land use, distance to road, and soil type was used to map the landslide hazard. This study used ROC (Receiver Operating Characteristic) analysis to validate the landslide susceptibility mapping with an AUC (Area Under Curve) value of 0.93, which indicates that the mapping has a high accuracy value. The results showed that the landslide susceptibility is divided into three classes: high susceptible, moderate susceptible, and low susceptible. The high susceptible area covers 151.62 km2 (21%), the moderate susceptible area covers 407.99 km2 (56%), and the low susceptible area covers 166.79 km2 (23%). Based on the results of the mapping, tourism development in the area of Mount Raung is recommended in areas that are classified as medium and low landslide susceptibility.Keywords: Mount Raung, Landslides, SMCE, Tourism Development

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Analysis of short- and long-term controls on the variability of event-based runoff coefficient

Xu,

Li,

Merwade

2024

Journal of Hydrology: Regional Studies

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The selective causative factors on landslide susceptibility assessment: Case study Takengon, Aceh, Indonesia

Cited by 6 publications

References 17 publications

Landslide susceptibility analysis in Kabandungan District and Salak Geothermal Field, West Java

Landslide susceptibility analysis in Kabandungan District and Salak Geothermal Field, West Java

Optimizing Tourism Development Through Landslide Hazard Mapping in Raung Volcano

Analysis of short- and long-term controls on the variability of event-based runoff coefficient

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