scholarly journals Exploring the Impact of Multitemporal DEM Data on the Susceptibility Mapping of Landslides

2020 ◽  
Vol 10 (7) ◽  
pp. 2518 ◽  
Author(s):  
Jiaying Li ◽  
Weidong Wang ◽  
Zheng Han ◽  
Yange Li ◽  
Guangqi Chen
Keyword(s):  
Logistic Regression ◽  
Temporal Variation ◽  
Landscape Change ◽  
Logistic Regression Model ◽  
Susceptibility Mapping ◽  
Susceptibility Assessment ◽  
Susceptibility Maps ◽  
Digital Elevation ◽  
The Impact ◽  
Topographic Variation

Digital elevation models (DEMs) are fundamental data models used for susceptibility assessment of landslides. Due to landscape change and reshaping processes, a DEM can show obvious temporal variation and has a significant influence on assessment results. To explore the impact of DEM temporal variation on hazard susceptibility, the southern area of Sichuan province in China is selected as a study area. Multitemporal DEM data spanning over 17 years are collected and the topographic variation of the landscape in this area is investigated. Multitemporal susceptibility maps of landslides are subsequently generated using the widely accepted logistic regression model (LRM). A positive correlation between the topographic variation and landslide susceptibility that was supported by previous studies is quantitatively verified. The ratio of the number of landslides to the susceptibility level areas (RNA) in which the hazards occur is introduced. The RNA demonstrates a general decrease in the susceptibility level from 2000 to 2009, while the ratio of the decreased level is more than fifteen times greater than that of the ratio of the increased level. The impact of the multitemporal DEM on susceptibility mapping is demonstrated to be significant. As such, susceptibility assessments should use DEM data at the time of study.

scholarly journals Work absence and multimorbidity in Portugal: results from the 1st National Health Examination Survey

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Matos ◽  
C Matias Dias ◽  
A Félix
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Chronic Diseases ◽  
National Health ◽  
Logistic Regression Model ◽  
Health Examination ◽  
Work Absence ◽  
Health Examination Survey ◽  
Absence From Work ◽  
The Impact

Abstract Background Studies on the impact of patients with multimorbidity in the absence of work indicate that the number and type of chronic diseases may increase absenteeism and that the risk of absence from work is higher in people with two or more chronic diseases. This study analyzed the association between multimorbidity and greater frequency and duration of work absence in the portuguese population between the ages of 25 and 65 during 2015. Methods This is an epidemiological, observational, cross-sectional study with an analytical component that has its source of information from the 1st National Health Examination Survey. The study analyzed univariate, bivariate and multivariate variables under study. A multivariate logistic regression model was constructed. Results The prevalence of absenteeism was 55,1%. Education showed an association with absence of work (p = 0,0157), as well as professional activity (p = 0,0086). It wasn't possible to verify association between the presence of chronic diseases (p = 0,9358) or the presence of multimorbidity (p = 0,4309) with absence of work. The prevalence of multimorbidity was 31,8%. There was association between age (p < 0,0001), education (p < 0,001) and yield (p = 0,0009) and multimorbidity. There is no increase in the number of days of absence from work due to the increase in the number of chronic diseases. In the optimized logistic regression model the only variables that demonstrated association with the variable labor absence were age (p = 0,0391) and education (0,0089). Conclusions The scientific evidence generated will contribute to the current discussion on the need for the health and social security system to develop policies to patients with multimorbidity. Key messages The prevalence of absenteeism and multimorbidity in Portugal was respectively 55,1% and 31,8%. In the optimized model age and education demonstrated association with the variable labor absence.

Influence of DEM spatial resolution on the susceptibility mapping with SHALSTAB in the Rio Garcia hydrographic basin, municipality of Blumenau/SC / Influência de la resolución espacial del MDE en la cartografía de susceptibilidad con SHALSTAB en la cuenca hidrográfica del Río García, municipio de Blumenau/SC

2021 ◽  
Vol 5 (3) ◽  
pp. 1475-1491
Author(s):  
Gisele Marilha Pereira Reginatto ◽  
Regiane Mara Sbroglia ◽  
Camilo Andrade Carreño ◽  
Bianca Rodrigues Schvartz ◽  
Pâmela Betiatto ◽  
...  
Keyword(s):  
Digital Elevation Model ◽  
Spatial Resolution ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Hydrographic Basin ◽  
Susceptibility Maps ◽  
Digital Elevation ◽  
Stability Model ◽  
Elevation Model ◽  
Del Rio

In translational landslide susceptibility analysis with SHALSTAB (Shallow Landsliding Stability Model), the resolution of the digital elevation model (DSM) is determinant for defining the type of mapping generated (preliminary or not). In this study, in order to verify the influence of the SDM scale on the SHALSTAB stability classes, susceptibility maps were prepared at two scales: 1:50,000 and 1:10,000. The study area was the Garcia River watershed, belonging to the municipality of Blumenau, Santa Catarina, affected by landslides in the 2008 catastrophe, which enabled the validation of the simulations with the scars mapped in the field. Thus, the influence of scale on the distribution of the model's stability classes and on its performance was verified. SHALSTAB performed better at the 1:10,000 scale, predicting 70% of the instabilities in a percentage of unstable area approximately three times smaller than at the 1,50,000 scale.

scholarly journals Landslide susceptibility mapping of the Sera River Basin using logistic regression model

2016 ◽  
Vol 85 (3) ◽  
pp. 1323-1346 ◽  
Author(s):  
Nussaïbah B. Raja ◽  
Ihsan Çiçek ◽  
Necla Türkoğlu ◽  
Olgu Aydin ◽  
Akiyuki Kawasaki
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
River Basin ◽  
Landslide Susceptibility ◽  
Logistic Regression Model ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping

Entrepreneurship Program at Secondary School

2019 ◽  
pp. 202-217
Author(s):  
Maria Jose Madeira ◽  
Andreia Rocha
Keyword(s):  
Logistic Regression ◽  
Secondary School ◽  
Regression Model ◽  
Extracurricular Activities ◽  
Secondary School Students ◽  
Logistic Regression Model ◽  
Secondary Level ◽  
Entrepreneurial Intentions ◽  
School Students ◽  
The Impact

The aim of this study is the analysis of the effect that the entrepreneurship activities have in the development of an entrepreneurial profile of the students of a secondary level and their future entrepreneurial intentions. To empirically test the formulated hypothesis, a questionnaire was developed. By the application of methods of factorial analysis and a logistic regression model, it was concluded that variables like the incentive given by school agents, the participation of students in extracurricular activities and the desire to continue studying, influenced the entrepreneurial intentions of the students. Concerning the entrepreneurial profile, it was concluded that the students who have higher capacity of creation and concretization of entrepreneurial projects and a winning will, have a higher propensity to create their own business. This research analysed the impact of entrepreneurship activities on the development of the secondary school students' entrepreneurial profile, and understand whether these same activities influence their future intentions.

scholarly journals Flash Flood Susceptibility Assessment Based on Geodetector, Certainty Factor, and Logistic Regression Analyses in Fujian Province, China

2020 ◽  
Vol 9 (12) ◽  
pp. 748
Author(s):  
Yifan Cao ◽  
Hongliang Jia ◽  
Junnan Xiong ◽  
Weiming Cheng ◽  
Kun Li ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Flash Flood ◽  
Pearson Correlation ◽  
Flash Floods ◽  
Ensemble Model ◽  
Susceptibility Assessment ◽  
Certainty Factor ◽  
Fujian Province ◽  
Flood Susceptibility ◽  
Susceptibility Maps

Flash floods are one of the most frequent natural disasters in Fujian Province, China, and they seriously threaten the safety of infrastructure, natural ecosystems, and human life. Thus, recognition of possible flash flood locations and exploitation of more precise flash flood susceptibility maps are crucial to appropriate flash flood management in Fujian. Based on this objective, in this study, we developed a new method of flash flood susceptibility assessment. First, we utilized double standards, including the Pearson correlation coefficient (PCC) and Geodetector to screen the assessment indicator. Second, in order to consider the weight of each classification of indicator and the weights of the indicators simultaneously, we used the ensemble model of the certainty factor (CF) and logistic regression (LR) to establish a frame for the flash flood susceptibility assessment. Ultimately, we used this ensemble model (CF-LR), the standalone CF model, and the standalone LR model to prepare flash flood susceptibility maps for Fujian Province and compared their prediction performance. The results revealed the following. (1) Land use, topographic relief, and 24 h precipitation (H24_100) within a 100-year return period were the three main factors causing flash floods in Fujian Province. (2) The area under the curve (AUC) results showed that the CF-LR model had the best precision in terms of both the success rate (0.860) and the prediction rate (0.882). (3) The assessment results of all three models showed that between 22.27% and 29.35% of the study area have high and very high susceptibility levels, and these areas are mainly located in the east, south, and southeast coastal areas, and the north and west low mountain areas. The results of this study provide a scientific basis and support for flash flood prevention in Fujian Province. The proposed susceptibility assessment framework may also be helpful for other natural disaster susceptibility analyses.

scholarly journals Impact of Spatial Resolution of Digital Elevation Model on Landslide Susceptibility Mapping: A case Study in Kullu Valley, Himalayas

Geosciences ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. 360 ◽  
Author(s):  
Sansar Raj ◽  
Thimmaiah
Keyword(s):  
Digital Elevation Model ◽  
Spatial Resolution ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Mountainous Regions ◽  
Digital Elevation ◽  
Kullu Valley ◽  
Elevation Model ◽  
The Impact

Landslides are one of the most damaging geological hazards in mountainous regions such as the Himalayas. The Himalayan region is, tectonically, the most active region in the world that is highly vulnerable to landslides and associated hazards. Landslide susceptibility mapping (LSM) is a useful tool for understanding the probability of the spatial distribution of future landslide regions. In this research, the landslide inventory datasets were collected during the field study of the Kullu valley in July 2018, and 149 landslide locations were collected as global positioning system (GPS) points. The present study evaluates the LSM using three different spatial resolution of the digital elevation model (DEM) derived from three different sources. The data-driven traditional frequency ratio (FR) model was used for this study. The FR model was used for this research to assess the impact of the different spatial resolution of DEMs on the LSM. DEM data was derived from Advanced Land Observing Satellite-1 (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) ALOS-PALSAR for 12.5 m, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global for 30 m, and the Shuttle Radar Topography Mission (SRTM) for 90 m. As an input, we used eight landslide conditioning factors based on the study area and topographic features of the Kullu valley in the Himalayas. The ASTER-Global 30m DEM showed higher accuracy of 0.910 compared to 0.839 for 12.5 m and 0.824 for 90 m DEM resolution. This study shows that that 30 m resolution is better suited for LSM for the Kullu valley region in the Himalayas. The LSM can be used for mitigation and future planning for spatial planners and developmental authorities in the region.

Landslide Susceptibility Mapping and Model Performance Assessment

2013 ◽  
Vol 353-356 ◽  
pp. 3487-3493 ◽  
Author(s):  
Chen Chao Xiao ◽  
Yuan Tian ◽  
Kang Ping Si ◽  
Ting Li
Keyword(s):  
Logistic Regression ◽  
Performance Assessment ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Model Performance ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Maps ◽  
Overall Performance ◽  
Better Than

In this paper landslide susceptibility mapping and model performance assessment was conducted using three models, logistic regression, GAM, and SVM, in a study area in Shenzhen, China. Ten factors, slope angle, aspect, elevation, plan and profile curvature of the slope, lithology, NDVI, building density, the distance to the river, and the distance to the fault were selected as influencing factors for the landslide occurrences. All three models were trained and the resulting susceptibility maps were created. The performances of the three models were then assessed by AUC values through a 10-fold cross-validation. It could be concluded that in the study area GAM had the best overall performance among the three models, while SVM was better than logistic regression. Based on the derived DPR values, the optimum thresholds between stable areas and risky areas for all three models were also determined.

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