Effect of Earthquake on Subsequent Typhoon-Induced Landslides Using Remote Sensing Imagery in the 99 Peaks Region, Central Taiwan

2012 ◽  
Vol 500 ◽  
pp. 773-779 ◽  
Author(s):  
Shing Tsz Lee ◽  
Teng To Yu ◽  
Wen Fei Peng
Keyword(s):  
Logistic Regression ◽  
Hazard Analysis ◽  
Grid Cell ◽  
Seismic Intensity ◽  
Landslide Hazard ◽  
Slope Aspect ◽  
Before And After ◽  
Best Fitting ◽  
Seismic Landslide ◽  
Central Taiwan

The effect of Chi-Chi earthquake on typhoon-triggered landslides was estimated using accuracy curves method in seismic landslide hazard model. The logistic regression model and geographic information system (GIS) are chosen to perform the seismic landslide hazard analysis. An inventory map of the landslides from SPOT images taken before and after the events was used to produce a dependent variable, which takes a value of 0 and 1 for the absence and presence of landslides. A set of independent parameters include lithology, elevation, slope gradient, slope aspect, terrain roughness, land use and Arias intensity (Ia) with topographic effect. Subsequently, the logistic regression is used to find the best fitting function to describe the relationship between occurrence or non-occurrence of landslides within an individual grid cell. The decreased effect of the earthquake was measured using accuracy curves method. It found that the effect of earthquake decreases with time. The landslide events of 2004 had little correlation with the Chi-Chi earthquake. Nevertheless, after period of 5 years, the seismic intensity from the Chi-Chi earthquake might still have affected conditions of landslides in the study area.

scholarly journals Incorporating the effects of topographic amplification in the analysis of earthquake-induced landslide hazards using logistic regression

2010 ◽  
Vol 10 (12) ◽  
pp. 2475-2488 ◽  
Author(s):  
S. T. Lee ◽  
T. T. Yu ◽  
W. F. Peng ◽  
C. L. Wang
Keyword(s):  
Logistic Regression ◽  
Hazard Analysis ◽  
Grid Cell ◽  
Landslide Hazard ◽  
Slope Aspect ◽  
Topographic Effect ◽  
Topographic Amplification ◽  
Landslide Hazards ◽  
Digital Elevation ◽  
Seismic Landslide

Abstract. Seismic-induced landslide hazards are studied using seismic shaking intensity based on the topographic amplification effect. The estimation of the topographic effect includes the theoretical topographic amplification factors and the corresponding amplified ground motion. Digital elevation models (DEM) with a 5-m grid space are used. The logistic regression model and the geographic information system (GIS) are used to perform the seismic landslide hazard analysis. The 99 Peaks area, located 3 km away from the ruptured fault of the Chi-Chi earthquake, is used to test the proposed hypothesis. An inventory map of earthquake-triggered landslides is used to produce a dependent variable that takes a value of 0 (no landslides) or 1 (landslides). A set of independent parameters, including lithology, elevation, slope gradient, slope aspect, terrain roughness, land use, and Arias intensity (Ia) with the topographic effect. Subsequently, logistic regression is used to find the best fitting function to describe the relationship between the occurrence and absence of landslides within an individual grid cell. The results of seismic landslide hazard analysis that includes the topographic effect (AUROC = 0.890) are better than those of the analysis without it (AUROC = 0.874).

Evaluation of Seismic Landslide Runout Distance Based on the Fuzzy Mathematics and Range Analysis

2013 ◽  
Vol 405-408 ◽  
pp. 2341-2345
Author(s):  
Yao Xun Zeng ◽  
Xiao Yi Fan
Keyword(s):  
Evaluation Model ◽  
Soil Mass ◽  
Seismic Intensity ◽  
Landslide Hazard ◽  
Slope Aspect ◽  
Fuzzy Mathematics ◽  
Range Analysis ◽  
Runout Distance ◽  
Seismic Landslide ◽  
Landslide Runout

The Runout Distance is One of the most Important Factors of Landslide Hazard. it Not only Controls the Hazard Area but also Influences on the Landslide Hazard Assessment. Based on the Data of Landslides Induced by the Wenchuan Earthquake, the Six Slope Structures, Including Rock and Soil Mass, Slope Type, Gradient (α), Slope Height ( h), Azimuth Difference between Slope-Aspect and Strata Trend(θ1), Difference between Slope Gradient and Obliquity of Rock Layer (θ2), and Seismic Intensity were Selected to Evaluate the Landslide Runout Distances. the Evaluation Model of Landslide Runout Distance was Proposed by the Combination of Fuzzy Mathematics and Range Analysis. the Results Indicated that it was Different that the Slope Structures Influenced on the Runout Distance in the Different Volume Scale. According to the Evaluation Model, the Typical Landslide Runout Distances were Studied, and the Evaluation Results Accord with the Actual Runout Distances.

scholarly journals Landslide hazard zonation using logistic regression technique: the case of Shafe and Baso catchments, Gamo highland, Ethiopia

2021 ◽  
Author(s):  
Leulalem Shano ◽  
Tarun Kumar Raghuvanshi ◽  
Matebie Meten
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Influencing Factors ◽  
Landslide Hazard ◽  
Slope Aspect ◽  
Hazard Map ◽  
Hazard Zonation ◽  
Landslide Hazard Zonation ◽  
Landslide Hazard Map ◽  
Very High

Abstract Landslide hazard zonation plays an important role in safe and viable infrastructure development, urbanization, land use, and environmental planning. The Shafe and Baso catchments are found in the Gamo highland which has been highly degraded by erosion and landslides thereby affecting the lives of the local people. In recent decades, recurrent landslide incidences were frequently occurring in this Highland region of Ethiopia in almost every rainy season. This demands landslide hazard zonation in the study area in order to alleviate the problems associated with these landslides. The main objectives of this study are to identify the spatiotemporal landslide distribution of the area; evaluate the landslide influencing factors and prepare the landslide hazard map. In the present study, lithology, groundwater conditions, distance to faults, morphometric factors (slope, aspect and curvature), and land use/land cover were considered as landslide predisposing/influencing factors while precipitation was a triggering factor. All these factor maps and landslide inventory maps were integrated using ArcGIS 10.4 environment. For data analysis, the principle of logistic regression was applied in a statistical package for social sciences (SPSS). The result from this statistical analysis showed that the landslide influencing factors like distance to fault, distance to stream, groundwater zones, lithological units and aspect have revealed the highest contribution to landslide occurrence as they showed greater than a unit odds ratio. The resulting landslide hazard map was divided into five classes: very low (13.48%), low (28.67%), moderate (31.62%), high (18%), and very high (8.2%) hazard zones which was then validated using the goodness of fit techniques and receiver operating characteristic curve (ROC) with an accuracy of 85.4. The high and very high landslide hazard zones should be avoided from further infrastructure and settlement planning unless proper and cost-effective landslide mitigation measures are implemented.

scholarly journals Landslide hazard assessment around MCT zone in Marsyangdi River basin, west Nepal

2017 ◽  
Vol 53 ◽  
pp. 93-98
Author(s):  
Subash Acharya ◽  
Dinesh Pathak
Keyword(s):  
Logistic Regression ◽  
River Basin ◽  
Hazard Assessment ◽  
Landslide Hazard ◽  
Slope Aspect ◽  
Hazard Map ◽  
Landslide Hazard Assessment ◽  
Hazard Zone ◽  
High Hazard ◽  
Landslide Hazard Map

In the hilly and mountainous terrain of Nepal, landslide is the most common natural hazard especially during prolong rainfall. Every year landslide cost lives and causes injuries. In order to address this problem, the best that can be done is to prepare the landslide hazard map of the area, apply mitigation measures and evacuate the high hazardous area, if necessary. Landslide hazard assessment is the primary tool so as to understand the nature and characteristics of the slope that are prone to failure. Logistic Regression Model is used for the preparation of landslide hazard map of the Besi Shahar-Tal area in Marsyangdi River basin in west Nepal. The causative factors such as elevation, slope, slope aspect, land use, geology, rainfall, lineament density, stream density are used. All the thematic layers of these parameters are prepared in GIS and logistic regression analysis is done by using Statistical Package for Social Science (SPSS). Five different hazard zones are separated namely very low hazard zone, low hazard zone, medium hazard zone, high hazard zone and very high hazard zone. The high hazard zone is lying along the Marsyangdi River and its tributaries.

scholarly journals GIS − Based Logistic Regression Models for Landslide Hazard Mapping: A Case Study From Ensaro District, North Ethiopia

2020 ◽  
Author(s):  
Abdurohman Adem ◽  
Suryabhagavan Venkata Karuturi ◽  
Tarun Kumar Raghuvanshi
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Google Earth ◽  
Landslide Hazard ◽  
Slope Aspect ◽  
Hazard Evaluation ◽  
Hazard Mapping ◽  
Data Set ◽  
Causative Factors

Abstract The present study was undertaken to identify landslides hazard prone areas in North Ethiopia. The landslide hazard in the present study area was evaluated by using the logistic regression model. Seven landslide causative factors were used for the landslide hazard evaluation, these are; slope gradient, slope aspect, elevation, proximity to streams, land-use/ land-cover, lithology and Normalized Difference Vegetation Index. Besides, for the present study landslides inventory data for the period of 2000 to 2018 was collected from the field survey and the Google earth image interpretation. The coefficient for the considered causative factors and classes were used for the identification of landslides hazard index using raster tool in ARCGIS environment. The prediction of the logistic regression model reveals that one third of the study area (32%) is under high hazard zone and the steep slopes and the elevated areas are most susceptible areas. The predicted landslides hazard zonation map is highly correlated with the training data set where 74% of it lies in the very high and high landslide hazard zones. Results of the area under the Receiver Operating Characteristic curve for the training sample, was found to be 0.76 while the area under the ROC curve of the validation sample was 0.71. Thus, the validation results has confirmed the rationality of adopted methodology, considered causative factors and their evaluation in producing LHZ map for the area. Further, the study has forwarded recommendations that can be followed to prevent and mitigate the adverse impact of landslides in the study area.

Preliminary Evaluation of a Weibull Function for Fitting Slow-Component Eye Velocity over the Time Course of Caloric-Induced Nystagmus

1989 ◽  
Vol 32 (3) ◽  
pp. 681-687 ◽  
Author(s):  
C. Formby ◽  
B. Albritton ◽  
I. M. Rivera
Keyword(s):  
Time Course ◽  
Slow Component ◽  
Weibull Function ◽  
Preliminary Evaluation ◽  
Mathematical Function ◽  
Before And After ◽  
Best Fitting ◽  
Eye Velocity ◽  
Fitting Parameters ◽  
Weibull Equation

We describe preliminary attempts to fit a mathematical function to the slow-component eye velocity (SCV) over the time course of caloric-induced nystagmus. Initially, we consider a Weibull equation with three parameters. These parameters are estimated by a least-squares procedure to fit digitized SCV data. We present examples of SCV data and fitted curves to show how adjustments in the parameters of the model affect the fitted curve. The best fitting parameters are presented for curves fit to 120 warm caloric responses. The fitting parameters and the efficacy of the fitted curves are compared before and after the SCV data were smoothed to reduce response variability. We also consider a more flexible four-parameter Weibull equation that, for 98% of the smoothed caloric responses, yields fits that describe the data more precisely than a line through the mean. Finally, we consider advantages and problems in fitting the Weibull function to caloric data.

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