Interaction of earthquake-triggered landslides and local relief: Evidence from the 2008 Wenchuan earthquake

Qin, Yigen; Zhang, Dongli; Zheng, Wenjun; Xiong, Jianguo; Chen, Gan; Duan, Lei; Liang, Shumin; Peng, Hui

doi:10.1007/s10346-022-01996-3

Cited by 4 publications

(2 citation statements)

References 68 publications

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“…Relative relief depicts significant fissures in slopes and reflects the energy available for slope failures and soil degradation (Pradhan and Kim, 2021;Qin et al, 2023). Relative relief (RR) is essential for characterizing any topography's morphological elements.…”

Section: Causative Factorsmentioning

confidence: 99%

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Soft computing machine learning applications for assessing regional-scale landslide susceptibility in the Nepal Himalaya

Manandhar,

Huynh,

Bhattarai

et al. 2024

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PurposeThis research is aimed at preparing landslide susceptibility using spatial analysis and soft computing machine learning techniques based on convolutional neural networks (CNNs), artificial neural networks (ANNs) and logistic regression (LR) models.Design/methodology/approachUsing the Geographical Information System (GIS), a spatial database including topographic, hydrologic, geological and landuse data is created for the study area. The data are randomly divided between a training set (70%), a validation (10%) and a test set (20%).FindingsThe validation findings demonstrate that the CNN model (has an 89% success rate and an 84% prediction rate). The ANN model (with an 84% success rate and an 81% prediction rate) predicts landslides better than the LR model (with a success rate of 82% and a prediction rate of 79%). In comparison, the CNN proves to be more accurate than the logistic regression and is utilized for final susceptibility.Research limitations/implicationsLand cover data and geological data are limited in largescale, making it challenging to develop accurate and comprehensive susceptibility maps.Practical implicationsIt helps to identify areas with a higher likelihood of experiencing landslides. This information is crucial for assessing the risk posed to human lives, infrastructure and properties in these areas. It allows authorities and stakeholders to prioritize risk management efforts and allocate resources more effectively.Social implicationsThe social implications of a landslide susceptibility map are profound, as it provides vital information for disaster preparedness, risk mitigation and landuse planning. Communities can utilize these maps to identify vulnerable areas, implement zoning regulations and develop evacuation plans, ultimately safeguarding lives and property. Additionally, access to such information promotes public awareness and education about landslide risks, fostering a proactive approach to disaster management. However, reliance solely on these maps may also create a false sense of security, necessitating continuous updates and integration with other risk assessment measures to ensure effective disaster resilience strategies are in place.Originality/valueLandslide susceptibility mapping provides a proactive approach to identifying areas at higher risk of landslides before any significant events occur. Researchers continually explore new data sources, modeling techniques and validation approaches, leading to a better understanding of landslide dynamics and susceptibility factors.

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Section: Causative Factorsmentioning

confidence: 99%

“…Relative relief depicts significant fissures in slopes and reflects the energy available for slope failures and soil degradation (Pradhan and Kim, 2021; Qin et al. , 2023).…”

Section: Study Area and Datamentioning

confidence: 99%

Soft computing machine learning applications for assessing regional-scale landslide susceptibility in the Nepal Himalaya

Manandhar,

Huynh,

Bhattarai

et al. 2024

View full text Add to dashboard Cite

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Spatial distribution of near-fault landslides along Litang fault zones, eastern Tibetan Plateau

Qin,

Zhang,

Zheng

et al. 2024

Geomorphology

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Reappraisal of the 2012 magnitude (M _W ) 6.7 Negros Oriental (Philippines) earthquake intensity and ShakeMap generation by using ESI-2007 environmental effects

Naik,

Rimando,

Mittal

et al. 2024

Geomatics, Natural Hazards and Risk

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Interaction of earthquake-triggered landslides and local relief: Evidence from the 2008 Wenchuan earthquake

Cited by 4 publications

References 68 publications

Soft computing machine learning applications for assessing regional-scale landslide susceptibility in the Nepal Himalaya

Soft computing machine learning applications for assessing regional-scale landslide susceptibility in the Nepal Himalaya

Spatial distribution of near-fault landslides along Litang fault zones, eastern Tibetan Plateau

Reappraisal of the 2012 magnitude (M _W ) 6.7 Negros Oriental (Philippines) earthquake intensity and ShakeMap generation by using ESI-2007 environmental effects

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Interaction of earthquake-triggered landslides and local relief: Evidence from the 2008 Wenchuan earthquake

Cited by 4 publications

References 68 publications

Soft computing machine learning applications for assessing regional-scale landslide susceptibility in the Nepal Himalaya

Soft computing machine learning applications for assessing regional-scale landslide susceptibility in the Nepal Himalaya

Spatial distribution of near-fault landslides along Litang fault zones, eastern Tibetan Plateau

Reappraisal of the 2012 magnitude (M W ) 6.7 Negros Oriental (Philippines) earthquake intensity and ShakeMap generation by using ESI-2007 environmental effects

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Reappraisal of the 2012 magnitude (M _W ) 6.7 Negros Oriental (Philippines) earthquake intensity and ShakeMap generation by using ESI-2007 environmental effects