Landslide Susceptibility Assessment Tools v1.0.0b – Project Manager Suite: A new modular toolkit for landslide susceptibility assessment

Torizin, Jewgenij; Schüßler, Nick; Fuchs, Michael

doi:10.5194/gmd-2021-247

Cited by 4 publications

(4 citation statements)

References 21 publications

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“…Whereas the instability of lower slopes covered with thick residual soils is largely initiated by a high water table (Zhuang et al, 2015), overall landslide frequency ratios generally increase with slope angles increasing to a maximum of 40 o . The increase is partly driven by gravitational forces from which greater shear stresses are generated along steeper slopes (Silalahi et al, 2019;Torizin, 2011). However, the decline in landslide frequency more vulnerable to landslides.…”

Section: Predisposing Landslide Conditioning Factorsmentioning

confidence: 99%

Co-seismic and rainfall triggered landslide hazard susceptibility framework for Uganda derived using fuzzy logic and geospatial modelling techniques

Oleng,

Ozdemir,

Pilakoutas

2024

Preprint

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Over the past few decades, Uganda has suffered from many damaging landslides like the 2010 Bududa, 1994 Kisomoro and 1966 Rwenzori events. Although most slope failures are rainfall-induced, earthquakes can also trigger larger scale landslides. Despite escalating landslide risk exacerbated by rapid population growth and urbanization coupled with a substandard building stock, comprehensive national co-seismic and rainfall-induced landslide hazard and risk maps for Uganda do not exist. This study aims to develop a co-seismic landslide hazard susceptibility framework using a geospatial-based fuzzy logic methodology. Initially landslide conditioning factor maps are created, aggregated with a stochastic event-based probabilistic seismic hazard model developed using OpenQuake-engine, and their spatial distribution is overlaid to produce final fuzzy sets describing relative probabilities of co-seismic landslide hazard. Seismic hazard map for a 475-year return period shows maximum bedrock peak ground acceleration increasing to 0.27 g in western Uganda. Further results show that areas of Precambrian granites located in upper slopes with gradients between 35 and 40o have the highest landslide frequency ratios. Whilst the majority of the country is exposed to relatively low hazard, the Rwenzori and Kigezi regions are highly susceptible to co-seismic landslides. To reduce the earthquake and landslide risks across Uganda, it is recommended that the directorate of disaster preparedness and management should develop suitable disaster risk mitigation measures like: local planning and zoning, structural retrofitting and infrastructure protection for key public buildings, natural system protection, and education towards raising community awareness.

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Section: Predisposing Landslide Conditioning Factorsmentioning

confidence: 99%

Co-seismic and rainfall triggered landslide hazard susceptibility framework for Uganda derived using fuzzy logic and geospatial modelling techniques

Oleng,

Ozdemir,

Pilakoutas

2024

Preprint

View full text Add to dashboard Cite

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“…Lithology, as the key parameter in LSA, defines the variety of geological units or rock formations that determine the topography's geo-mechanical features. Every unit has individual characteristics, deriving a distinctive resistance on each unit to ground motion (Torizin, 2011;Costanzo et al, 2012;Gautam et al, 2021).…”

Section: Datamentioning

confidence: 99%

Susceptibility Assessment of Earthquake-Induced Landslides: The 2018 Palu, Sulawesi Mw 7.5 Earthquake, Indonesia

Arifianti,

Pamela,

Iqbal

et al. 2023

MGPB

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A catastrophic Palu earthquake on September 28th, 2018 with Mw 7.5 triggered countless slope failures, generating numerous landslides. This paper presents a practical method for susceptibility assessment of earthquake-induced landslides in the Palu region and the surrounding area. The statistical weight of evidence (WoE) model was used to assess the relationship between landslides induced by seismic motion and its causative factors to determine the susceptibility level and derive an earthquake-induced landslide susceptibility map of this study area. The 1273 landslides were classified into two data series, training data for modelling (70%) and test data for validation (30%). The six selected thematic maps as landslide causative factors are lithology, land use, peak ground acceleration (PGA), and slope (gradient, aspect, elevation). The selection of causative factors considerably influences the frequency of landslides in the area. The result is satisfactory because the AUC value of the chosen model excelled the minimum limit, which is 0.6 (60%). The estimated success rate of the model is 85.7%, which shows that the relevancy of the model is good with the occurrence of landslides. The prediction rate of 84.6% indicates that the applied model is very good at predicting new landslides.

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“…2b). A slope gradient triggers the initiation of mass movement due to gravity effects acting on the slope acting as a sliding plane (Torizin, 2011). However, mass removal is highly accelerated when a threshold of the surface angle of inclination reaches 30° (Tofelde et al, 2017).…”

Section: Slope Anglementioning

confidence: 99%

GIS-based analysis of landslides susceptibility mapping: a case study of Lushoto district, north-eastern Tanzania

Makonyo

Zahor

2023

Nat Hazards

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Landslides are becoming increasingly widespread, claiming tens of thousands of fatalities, hundreds of thousands of injuries, and billions of dollars in economic losses each year. Thus, studies for geographically locating landslides vulnerable areas have been increasingly relevant in recent decades. This research is aimed at integrating Geographical Information Systems (GIS) and Remote Sensing (RS) techniques to delineate Landslide Susceptibility Mapping (LSM) of Lushoto District, Tanzania. RS assisted in providing remotely datasets including; Digital Elevation Models (DEM), Landsat 8 OLI imageries, and spatially distributed landslides coordinates with the use of a handheld Global Position System (GPS) receiver while various GIS analysis techniques were used in the preparation and analysis of landslides in uencing factors hence, generating LSM index values. However, rainfall, slope's angle, elevation, soil type, lithology, proximity to roads, rivers, faults, and Normalized Difference Vegetation Index (NDVI) factors were found to have direct in uence on the occurrence of landslides. These factors were evaluated, weighted, and ranked using Analytical Hierarchy Process (AHP) technique in which 0.086 (8.6%) consistency ratio (CR) was attained (highly accepted). Findings reveal that, rainfall (29.97%), slopes' angle (21.72%), elevation (15.68%), and soil types (11.77%) were found to have high in uence on the occurrence of landslides while proximity to faults (8.35%), lithology (4.94%), proximity to roads (3.41%), rivers (2.48%) and NDVI (1.69%) had very low in uences respectively. The overall results, obtained through Weighted Linear Combination (WLC) analysis indicate that, about 97669.65 hectares (ha) of the land is under very low landslides susceptibility levels which accounts for 24.03% of the total study area. Low susceptibility levels had 123105.84 ha (30.28%) moderate landslides susceptibility areas were found to have 140264.79 ha (34.50%) while high and very high susceptibility areas were found to cover about 45423.43 ha (11.17%) and 57.78 ha (0.01%) respectively. Furthermore, 81% overall model accuracy was obtained as computed from Area under the Curve (AUC) using Receiver Operating Characteristic (ROC) Curve.

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Landslide Susceptibility Assessment Tools v1.0.0b – Project Manager Suite: A new modular toolkit for landslide susceptibility assessment

Cited by 4 publications

References 21 publications

Co-seismic and rainfall triggered landslide hazard susceptibility framework for Uganda derived using fuzzy logic and geospatial modelling techniques

Co-seismic and rainfall triggered landslide hazard susceptibility framework for Uganda derived using fuzzy logic and geospatial modelling techniques

Susceptibility Assessment of Earthquake-Induced Landslides: The 2018 Palu, Sulawesi Mw 7.5 Earthquake, Indonesia

GIS-based analysis of landslides susceptibility mapping: a case study of Lushoto district, north-eastern Tanzania

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