Landslides triggered by the 3 August 2014 Ludian earthquake in China: geological properties, geomorphologic characteristics and spatial distribution analysis

Zhou, Jia‐wen; Lu, Pan; Hao, Ming‐hui

doi:10.1080/19475705.2015.1075162

Cited by 29 publications

(9 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In many earthquake events, earthquake-triggered landslides were strongly controlled by seismogenic faults [50]. Examples include the 1992 Haiyuan [51][52][53], 2008 Wenchuan, Sichuan [54,55], 2010 Yushu, [24], 2013 Minxian, Gansu [56], 2014 Ludian [57], 2015 Nepal [58], and 2017 Jiuzhaigou, Sichuan [59] earthquakes.…”

Section: Discussionmentioning

confidence: 99%

Effects of Seismogenic Faults on the Predictive Mapping of Probability to Earthquake-Triggered Landslides

Shao

et al. 2019

IJGI

View full text Add to dashboard Cite

The seismogenic fault is crucial for spatial prediction of co-seismic landslides, e.g., in logistic regression (LR) analysis considering influence factors. On one hand, earthquake-induced landslides are usually densely distributed along the seismogenic fault; on the other hand, different sections of the seismogenic fault may have distinct landslide-triggering capabilities due to their different mechanical properties. However how the feature of a fault influence mapping of landslide occurrence probability remains unclear. Relying on the landslide data of the 2013 Lushan, China Mw 6.6 earthquake, this study attempted to further address this issue. We quantified the seismogenic fault effects on landslides into three modes: the distance effect, the different part effects, and the combined effects of the two. Four possible cases were taken into consideration: zoning the study area vertical and parallel to the fault (case 1), zoning the study area only vertical to the fault (case 2), zoning the study area only parallel to the fault (case 3), and without such study-area zonations (case 4). Using the LR model, predictive landslide probability maps were prepared on these four cases. The model also fully considered other influencing factors of earthquake landslides, including elevation, slope, aspect, topographic wetness index (TWI), peak ground acceleration (PGA), lithology, rainfall, distance from the epicenter, distance from the road, and distance from the river. Then, cross-comparisons and validations were conducted on these maps. For training datasets, results show that the success rates of earthquake-triggered landslides for the former three scenarios were 85.1%, 84.2%, and 84.7%, respectively, while that of the model for case 4 was only 84%. For testing datasets, the prediction rates of the four LRs were 84.45%, 83.46%, 84.22%, and 83.61%, respectively, as indicated by comparing the test dataset and the landslide probability map. This means that the effects of the seismogenic fault, which are represented by study-area zonations vertical and parallel to the fault proper, are significant to the predictive mapping of earthquake-induced landslides.

show abstract

Section: Discussionmentioning

confidence: 99%

Effects of Seismogenic Faults on the Predictive Mapping of Probability to Earthquake-Triggered Landslides

Shao

et al. 2019

IJGI

View full text Add to dashboard Cite

show abstract

“…Ludian County is considered a seismic zone. In fact, there have been more than 15 earthquakes with magnitudes of Mw 6.0 or larger in Ludian since 1900 [35]. Thus, landslides induced by earthquakes pose high risks to the people living in the area.…”

Section: Description Of the Study Areamentioning

confidence: 99%

Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping

et al. 2021

View full text Add to dashboard Cite

This study proposed a new hybrid model based on the convolutional neural network (CNN) for making effective use of historical datasets and producing a reliable landslide susceptibility map. The proposed model consists of two parts; one is the extraction of landslide spatial information using two-dimensional CNN and pixel windows, and the other is to capture the correlated features among the conditioning factors using one-dimensional convolutional operations. To evaluate the validity of the proposed model, two pure CNN models and the previously used methods of random forest and a support vector machine were selected as the benchmark models. A total of 621 earthquake-triggered landslides in Ludian County, China and 14 conditioning factors derived from the topography, geological, hydrological, geophysical, land use and land cover data were used to generate a geospatial dataset. The conditioning factors were then selected and analyzed by a multicollinearity analysis and the frequency ratio method. Finally, the trained model calculated the landslide probability of each pixel in the study area and produced the resultant susceptibility map. The results indicated that the hybrid model benefitted from the features extraction capability of the CNN and achieved high-performance results in terms of the area under the receiver operating characteristic curve (AUC) and statistical indices. Moreover, the proposed model had 6.2% and 3.7% more improvement than the two pure CNN models in terms of the AUC, respectively. Therefore, the proposed model is capable of accurately mapping landslide susceptibility and providing a promising method for hazard mitigation and land use planning. Additionally, it is recommended to be applied to other areas of the world.

show abstract

“…Moreover, around 6% of the landslides cover less than 1000 m 2 in area. More information are available in Zhou et al (2016b) and Zhou et al (2016a).…”

Section: Study Areamentioning

confidence: 99%

Novel hybrids of adaptive neuro-fuzzy inference system (ANFIS) with several metaheuristic algorithms for spatial susceptibility assessment of seismic-induced landslide

Moayedi

Mehrabi

Kalantar

et al. 2019

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

Strong ground motions usually trigger lots of slope failures in the affected area. In this work, we analyse the occurrence likelihood of earthquake-triggered landslide by employing the ensembles of adaptive neuro-fuzzy inference systems (ANFIS) with four well-known metaheuristics techniques, namely particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), and differential evolution (DE) algorithms. Twelve landslide conditioning factors namely, elevation, slope degree, lithology, peak ground acceleration (PGA), stream power index (SPI), topographic wetness index (TWI), distance to road, distance to river, distance to fault, normalized difference vegetation index (NDVI), slope aspect, and plan curvature are considered within the geographic information system (GIS) to produce the required spatial database. In this paper, frequency ratio (FR) model is used to evaluate the spatial interaction between the landslides and conditioning factors. Meantime, among a total of 458 marked earthquake-induced landslides, 366 (80%) are specified to the learning process, and the remaining 92 (20%) landslides are used to evaluate the accuracy of applied models. The landslide susceptibility maps are generated in the GIS environment. Three accuracy criteria of mean square error (MSE), root mean square error (RMSE), and area under the receiving operating characteristic curve (AUROC) are used to develop a ranking system for comparing the integrity of the designed models. The total ranking scores (TRSs) of 15, 8, 10, and 18, respectively, obtained for PSO-ANFIS, GA-ANFIS, ACO-ANFIS, and DE-ANFIS revealed the superiority of the DE algorithm compared to other metaheuristics techniques. Also, the DE-ANFIS emerged as the fastest ensemble, due to the highest convergence speed obtained for this model.

show abstract

Landslides triggered by the 3 August 2014 Ludian earthquake in China: geological properties, geomorphologic characteristics and spatial distribution analysis

Cited by 29 publications

References 23 publications

Effects of Seismogenic Faults on the Predictive Mapping of Probability to Earthquake-Triggered Landslides

Effects of Seismogenic Faults on the Predictive Mapping of Probability to Earthquake-Triggered Landslides

Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping

Novel hybrids of adaptive neuro-fuzzy inference system (ANFIS) with several metaheuristic algorithms for spatial susceptibility assessment of seismic-induced landslide

Contact Info

Product

Resources

About