Susceptibility Analysis of Geohazards in the Longmen Mountain Region after the Wenchuan Earthquake

Li, Shuai; Ni, Zhongyun; Zhao, Yinbing; Hu, Wei; Long, Zhenrui; Ma, Haiyu; Zhou, Guoli; Luo, Yuhao; Geng, Chuntao

doi:10.3390/ijerph19063229

Cited by 10 publications

(8 citation statements)

References 109 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In DA, the NDVI value changed greatly from 2007 to 2009 ( Figure 6 ). Due to the influence of the Wenchuan earthquake, the relatively strong vibration around the fault caused varying degrees of vegetation damage [ 67 ]. Secondary disasters such as landslides and collapses caused by the earthquake in this area gradually transformed the forest land into grassland or bare land [ 68 , 69 ].…”

Section: Resultsmentioning

confidence: 99%

Spatial-Temporal Evolution and Driving Forces of NDVI in China’s Giant Panda National Park

Zhao

et al. 2022

IJERPH

Self Cite

View full text Add to dashboard Cite

Identifying the ecological evolution trends and vegetation driving mechanisms of giant panda national parks can help to improve the protection of giant panda habitats. Based on the research background of different geomorphological zoning, we selected the MODIS NDVI data from 2000 to 2020 to analyze the NDVI trends using a univariate linear model. A partial correlation analysis and multiple correlation analysis were used to reveal the influence of temperature and precipitation on NDVI trends. Fourteen factors related to meteorological factors, topographic factors, geological activities, and human activities were selected, and the Geographically Weighted Regression model was used to study the mechanisms driving NDVI change. The results were as follows: (1) The NDVI value of Giant Panda National Park has fluctuated and increased in the past 21 years, with an annual growth rate of 4.7%/yr. Affected by the Wenchuan earthquake in 2008, the NDVI value fluctuated greatly from 2008 to 2012, and reached its peak in 2018. (2) The NDVI in 94% of the study area improved, and the most significant improvement areas were mainly distributed in the northern and southern regions of Southwest Subalpine and Middle Mountain and the Xiaoxiangling area. Affected by the distribution of fault zones and their local activities, vegetation degradation was concentrated in the Dujiangyan–Anzhou area of Hengduan Mountain Alpine Canyon. (3) The Geographically Weighted Regression analysis showed that natural factors were dominant, with climate and elevation having a double-factor enhancement effect, the peak acceleration of ground motion and fault zone having a superimposed effect, and river density and slope having a double effect, all of which had a significant impact on the NDVI value of the surrounding area. To optimize the ecological security pattern of the Giant Panda National Park, we recommended strengthening the construction of ecological security projects through monitoring meteorological changes, preventing, and controlling geo-hazards, and optimizing the layout and intensity of human activities.

show abstract

Section: Resultsmentioning

confidence: 99%

Spatial-Temporal Evolution and Driving Forces of NDVI in China’s Giant Panda National Park

Zhao

et al. 2022

IJERPH

Self Cite

View full text Add to dashboard Cite

show abstract

“…Topography plays an important role in controlling geological hazards and determines the spatial distribution of geological hazard susceptibility to a large extent [ 36 ]. The study area is mainly mountainous, with a large difference in elevation between mountains and plains.…”

Section: Discussionmentioning

confidence: 99%

“…Elevation (E): Elevation is an inherent assessment indicator of landslide occurrence [ 36 ]. The study area is a hilly area with a maximum elevation difference of 1834 m. Since many indicators are changing with the dramatic ups and downs in the study area, the elevation becomes an indispensable indicator affecting the occurrence of landslides.…”

Section: Methodsmentioning

confidence: 99%

Refined Zoning of Landslide Susceptibility: A Case Study in Enshi County, Hubei, China

Wang

Qiu

et al. 2022

IJERPH

View full text Add to dashboard Cite

At present, landslide susceptibility assessment (LSA) based on the characteristics of landslides in different areas is an effective prevention measure for landslide management. In Enshi County, China, the landslides are mainly triggered by high-intensity rainfall, which causes a large number of casualties and economic losses every year. In order to effectively control the landslide occurrence in Enshi County and mitigate the damages caused by the landslide. In this study, eight indicators were selected as assessment indicators for LSA in Enshi County. The analytic hierarchy process (AHP) model, information value (IV) model and analytic hierarchy process-information value (AHP-IV) model were, respectively, applied to assess the landslide distribution of landslides in the rainy season (RS) and non-rainy season (NRS). Based on the three models, the study area was classified into five levels of landslide susceptibility, including very high susceptibility, high susceptibility, medium susceptibility, low susceptibility, and very low susceptibility. The receiver operating characteristic (ROC) curve was applied to verify the model accuracy. The results showed that the AHP-IV model (ROC = 0.7716) was more suitable in RS, and the IV model (ROC = 0.8237) was the most appropriate model in NRS. Finally, combined with the results of landslide susceptibility in RS and NRS, an integrated landslide susceptibility map was proposed, involving year-round high susceptibility, RS high susceptibility, NRS high susceptibility and year-round low susceptibility. The integrated landslide susceptibility results provide a more detailed division in terms of the different time periods in a year, which is beneficial for the government to efficiently allocate landslide management funds and propose effective landslide management strategies. Additionally, the focused arrangement of monitoring works in landslide-prone areas enable collect landslide information efficiently, which is helpful for the subsequent landslide preventive management.

show abstract

“…The developed cracks lead to rock and soil breakage, increase the water richness of rock layers, and accelerate rock erosion. In addition, a large number of secondary small faults are often developed near the large fault structural belt [52,53]. Regional tectonics leads to the complexity of geological conditions in the study area and aggravates the probability of landslide disasters.…”

Section: Geological and Hydrogeological Factorsmentioning

confidence: 99%

Mapping Landslide Sensitivity Based on Machine Learning: A Case Study in Ankang City, Shaanxi Province, China

Zhao

Zhu

et al. 2022

Geofluids

View full text Add to dashboard Cite

The main purpose of this research is to apply the logistic regression (LR) model, the support vector machine (SVM) model based on radial basis function, the random forest (RF) model, and the coupled model of the whale optimization algorithm (WOA) and genetic algorithm (GA) with RF, to make landslide susceptibility mapping for the Ankang City of Shaanxi Province, China. To this end, a landslide inventory map consisting of 4278 identified landslides is randomly divided into training and test landslides in a ratio of 7 : 3. The 15 landslide influencing factors are selected as follows: slope aspect, slope degree, elevation, terrain curvature, plane curvature, profile curvature, surface roughness, distance to faults, distance to roads, landform, lithology, distance to rivers, rainfall, stream power index (SPI), and normalized difference vegetation index (NDVI), and the potential multicollinearity problem among these factors is detected by Pearson correlation coefficient (PCC), variance inflation factor (VIF), and tolerance (TOL). We evaluate the performance of the model separately by statistical training and test dataset metrics, including sensitivity, specificity, accuracy, kappa, mean absolute error (MSE), root mean square error (RMSE), and area under the receiver operating characteristic curve. The training success rates of LR, SVM, RF, WOA-RF, and GA-RF models are 0.7546, 0.8317, 0.8561, 0.8804, and 0.8957; the testing success rates are 0.7551, 0.8375, 0.8395, 0.8348, and 0.85007. The results show that the GA significantly improves the predictive power of the RF model. This study provides a scientific reference for disaster prevention and control in this area and its surrounding areas.

show abstract

Susceptibility Analysis of Geohazards in the Longmen Mountain Region after the Wenchuan Earthquake

Cited by 10 publications

References 109 publications

Spatial-Temporal Evolution and Driving Forces of NDVI in China’s Giant Panda National Park

Spatial-Temporal Evolution and Driving Forces of NDVI in China’s Giant Panda National Park

Refined Zoning of Landslide Susceptibility: A Case Study in Enshi County, Hubei, China

Mapping Landslide Sensitivity Based on Machine Learning: A Case Study in Ankang City, Shaanxi Province, China

Contact Info

Product

Resources

About