Assessment of co-seismic landslide susceptibility using LR and ANCOVA in Barpak region, Nepal

Shrestha, Suchita; Kang, Tae Cheon; Choi, Jung Chang

doi:10.1007/s12040-018-0936-1

Cited by 20 publications

(15 citation statements)

References 48 publications

(45 reference statements)

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“…Statistical methods also helped to generate susceptibility maps based on the coseismic landslide distribution of the 2005 Kashmir , 2008 Wenchuan (Y. Wang, Song, et al, 2016), and 2015 Gorkha (Shrestha et al, 2018) earthquakes. Yet maps cannot readily be used to assess landslide susceptibility in future earthquakes with likely different characteristics and different landslide patterns.…”

Section: Statistical Modelsmentioning

confidence: 99%

Earthquake‐Induced Chains of Geologic Hazards: Patterns, Mechanisms, and Impacts

Fan

Scaringi

Korup

et al. 2019

Reviews of Geophysics

614

325

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Large earthquakes initiate chains of surface processes that last much longer than the brief moments of strong shaking. Most moderate‐ and large‐magnitude earthquakes trigger landslides, ranging from small failures in the soil cover to massive, devastating rock avalanches. Some landslides dam rivers and impound lakes, which can collapse days to centuries later, and flood mountain valleys for hundreds of kilometers downstream. Landslide deposits on slopes can remobilize during heavy rainfall and evolve into debris flows. Cracks and fractures can form and widen on mountain crests and flanks, promoting increased frequency of landslides that lasts for decades. More gradual impacts involve the flushing of excess debris downstream by rivers, which can generate bank erosion and floodplain accretion as well as channel avulsions that affect flooding frequency, settlements, ecosystems, and infrastructure. Ultimately, earthquake sequences and their geomorphic consequences alter mountain landscapes over both human and geologic time scales. Two recent events have attracted intense research into earthquake‐induced landslides and their consequences: the magnitude M 7.6 Chi‐Chi, Taiwan earthquake of 1999, and the M 7.9 Wenchuan, China earthquake of 2008. Using data and insights from these and several other earthquakes, we analyze how such events initiate processes that change mountain landscapes, highlight research gaps, and suggest pathways toward a more complete understanding of the seismic effects on the Earth's surface.

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Section: Statistical Modelsmentioning

confidence: 99%

Earthquake‐Induced Chains of Geologic Hazards: Patterns, Mechanisms, and Impacts

Fan

Scaringi

Korup

et al. 2019

Reviews of Geophysics

614

325

View full text Add to dashboard Cite

show abstract

“…We subjected the 14 CFs to multicollinearity testing to select optimal variables. Variance inflation (VIF) and tolerance (TOL) are widely used indices of the extent of multicollinearity (Kavzoglu et al 2014;Pradhan et al 2017;Shrestha et al 2018). A VIF ≥ 5 and a TOL < 0.2 indicate a major multicollinearity problem (Menard 1995;O'Brien 2007).…”

Section: Shallow Slide Susceptibility and Classificationmentioning

confidence: 99%

A shallow slide prediction model combining rainfall threshold warnings and shallow slide susceptibility in Busan, Korea

2018

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Rainfall-induced shallow landslides are common in many mountainous countries. Highly concentrated precipitation triggers landslides and debris flows in worldwide. Every year, several shallow slides and debris flows occur in Busan, South Korea during heavy rainfall. To reduce and prevent associated damage, we developed a matrix-based approach of rainfall threshold and landslide susceptibility. This study collects landslide inventories which consist of information of 260 landslide location, 35 landslide event times, and corresponding rainfall intensities and durations. A rainfall threshold warning levels were established using rainfall intensities and durations associated with 35 historical shallow slides, and categorised as null (< 5%), watch (5-20%), attention (20-50%), and alarm (> 50%). We used a back propagation ANN machine-learning algorithm to explore the effects of 14 causative factors on shallow slide distribution. The area under the curve was used to assess accuracy, and accuracy was found to be 86.12%. The derived rainfall threshold warning levels were assigned in rows and the susceptibility classes were used in columns of the matrix. The combined result represents the shallow slide hazards to rainfall threshold warning levels with a varying likelihood to shallow slide initiation. The results aim to raise awareness towards landslide hazards and to support regional decision for the land-use planning.

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“…6 (a). From the analysis of the distribution of the earthquake induced landslides we find a positive correlation between the number of landslides and PGA values (Shrestha et al, 2018). The study shows that the landslide distribution is maximum for area with higher PGA.…”

Section: Pgamentioning

confidence: 65%

Landslide susceptibility mapping using Weight of Evidence Method in Haku, Rasuwa District, Nepal

Gadtaula

Dhakal

2019

Journal of Nepal Geological Society

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The 2015 Gorkha Earthquake resulted in many other secondary hazards affecting the livelihoods of local people residing in mountainous area. Plenty of earthquake induced landslides and mass movement activities were observed after earthquake. Haku region of Rasuwa was also one of the severely affected areas by co-seismic landslides triggered by the disastrous earthquake. Statistics shows that around 400 families were relocated from Haku Post-earthquake (MoFA, 2015). A total of 101 co-seismic landslides were focused during the study and were verified during the fieldwork in Haku village. The conditioning factors used in this study were slope, aspect, elevation, curvature (plan and profile), landuse, geology and PGA. The conditioning factor maps were prepared in GIS working environment and further analysis was conducted with the assistance of Google earth. This study used Weight of Evidence (WoE), a bivariate statistical model and its performance was assessed. The susceptibility map was further characterized into five different classes namely very low, low, high, medium and very high susceptibility zones. The statistical analysis obtained from the results of the susceptibility map prepared by using WoE model gave the results that maximum area percentage of landslide distribution was observed in medium and high susceptibility classes i.e. 38% and 33% followed by very high (13%), low (10%) and very low classes (5.8%) About 25% of the total landslides are separated to validate the prepared model used in the landslide susceptibility zonation. The overlay method predicts the reliability of the model.

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Assessment of co-seismic landslide susceptibility using LR and ANCOVA in Barpak region, Nepal

Cited by 20 publications

References 48 publications

Earthquake‐Induced Chains of Geologic Hazards: Patterns, Mechanisms, and Impacts

Earthquake‐Induced Chains of Geologic Hazards: Patterns, Mechanisms, and Impacts

A shallow slide prediction model combining rainfall threshold warnings and shallow slide susceptibility in Busan, Korea

Landslide susceptibility mapping using Weight of Evidence Method in Haku, Rasuwa District, Nepal

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