An Integrated Model for the Geohazard Accident Duration on a Regional Mountain Road Network Using Text Data

Bai, Shumin; Ji, Xiaofeng; Dai, Bingyou; Pu, Yongming; Qin, Wenwen

doi:10.3390/su141912429

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…In contrast to the results obtained in scenarios with binary predictor variables, in this case, the performance of Random Survival Forest is superior compared to the Survival Support Vector Machine with 3 kinds of Kernel Functions and applies to all n used in this study, then in terms of performance in proceed by SSVM(ADD), SSVM(RBF) and SSVM(LIN) methods. This is also in accordance with research that was conducted by [19] & [20] in real case with mixed predictor variables, the result was that RSF was better at predicting compared to CPH and SSVM.…”

Section: Simulation Using Mixed Predictor Variablessupporting

confidence: 91%

Comparative Study of Survival Support Vector Machine and Random Survival Forest in Survival Data

Suantari,

Fitrianto,

Sartono

2023

BAREKENG: J. Math. & App.

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Survival analysis is a statistical procedure in analyzing data with the response variable is time until an event occurs (time-to-event). In the last few years, many classification approaches have been developed in machine learning, but only a few considered the presence of time-to-event variable. Random Survival Forest and Survival Support Vector Machine are machine learning approach which is a nonparametric classification method when dealing with large data and a response variable of survival time. Random Survival Forest is tree based method that using boostrapping algorithm, and Survival Support Vector Machine using hybrid approaches between regression and ranking constrain. The data used in this study is generated data in the form of right-censored survival data. This study uses the RandomForestSRC and SurvivalSVM packages on R software. This study aimed to compare the performance of the Survival Support Vector Machine and Random Survival Forest methods using simulation studies. Simulation results on right-censored survival data using binary predictor variables scenario indicate that the Survival Support Vector Machine (SSVM) method with Radial Basic Function Kernel (RBF Kernel) has the best model performance on data with small volumes, whereas when the data volume becomes larger, the method that has the best performance is Survival Support Vector Machine using Additive Kernel. Meanwhile, Random Survival Forest is a method that has the best performance for all conditions in mixed predictor variables scenario. Method, proportion of censored data and size of data are factors that affect the model performance.

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Section: Simulation Using Mixed Predictor Variablessupporting

confidence: 91%

Comparative Study of Survival Support Vector Machine and Random Survival Forest in Survival Data

Suantari,

Fitrianto,

Sartono

2023

BAREKENG: J. Math. & App.

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“…Unlike Kaplan-Meier curves and logrank tests, which are commonly used for monovariate analysis, Cox can be used for multifactor survival analysis. Moreover, Cox can include categorical variables (e.g., gender) and also numerical variables (e.g., age), whereas Kaplan-Meier curves and logrank tests can only include categorical variables [28,29]. Cox has a wider application since it extends survival analysis to simultaneously evaluate the effect of numerous risk factors on survival time [30].…”

Section: Model Constructionmentioning

confidence: 99%

Research on Mortality Risk of Chinese Older Adults from the Perspective of Social Health

2022

Sustainability

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In response to the increasing aging problem that China is facing, this study aimed to investigate the impact of social health on the mortality risk of the Chinese older adult population. In this paper, we used the data from “The Chinese Longitudinal Health Longevity Survey (CLHLS)-Longitudinal Data (2008–2018)” and applied the Cox proportional hazard model to investigate the effects of three dimensions of social health on the risk of death among older adults in China. The study found that: (1) The three dimensions of social health have a positive effect on the mortality risk of older adults. (2) Among those three dimensions, social activities had the most significant effect on older adults. The effect of social support and social network on the mortality risk of older adults are basically similar. Moreover, social activity, social support, and the social network had no significant effect on the young-old’s mortality risk. (3) In order to address the problem of data censoring, the Cox proportional hazard model can be used, whereas demographic characteristics, health status, and health behavior components are selective to society. This study enriches social health research in China and promotes the development of social health research from theory to practice. Moreover, it has obvious advantages in terms of economics and feasibility for achieving mortality risk reduction through improving the social health of older adults and building a healthy aging society.

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Road Accessibility during Natural Hazards Based on Volunteered Geographic Information Data and Network Analysis

Florath,

Chanussot,

Keller

2024

IJGI

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Natural hazards can present a significant risk to road infrastructure. This infrastructure is a fundamental component of the transportation infrastructure, with significant importance. During emergencies, society heavily relies on the functionality of the road infrastructure to facilitate evacuation and access to emergency facilities. This study introduces a versatile, multi-scale framework designed to analyze accessibility within road networks during natural hazard scenarios. The first module of the framework focuses on assessing the influence of natural hazards on road infrastructure to identify damaged or blocked road segments and intersections. It relies on near real-time information, often provided by citizen science through Volunteered Geographic Information (VGI) data and Natural Language Processing (NLP) of VGI texts. The second module conducts network analysis based on freely available Open Street Map (OSM) data, differentiating between intact and degraded road networks. Four accessibility measures are employed: betweenness centrality, closeness centrality, a free-flow assumption index, and a novel alternative routing assumption measure considering congestion scenarios. The study showcases its framework through an exemplary application in California, the United States, considering different hazard scenarios, where degraded roads and connected roads impacted by the hazard can be identified. The road extraction methodology allows the extraction of 75% to 100% of the impacted roads mentioned in VGI text messages for the respective case studies. In addition to the directly extracted impacted roads, constructing the degraded network also involves finding road segments that overlap with hazard impact zones, as these are at risk of being impacted. Conducting the network analysis with the four different measures on the intact and degraded network, changes in network accessibility due to the impacts of hazards can be identified. The results show that using each measure is justified, as each measure could demonstrate the accessibility change. However, their combination and comparison provide valuable insights. In conclusion, this study successfully addresses the challenges of developing a generic, complete framework from impact extraction to network analysis independently of the scale and characteristics of road network types.

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An Integrated Model for the Geohazard Accident Duration on a Regional Mountain Road Network Using Text Data

Cited by 3 publications

References 37 publications

Comparative Study of Survival Support Vector Machine and Random Survival Forest in Survival Data

Comparative Study of Survival Support Vector Machine and Random Survival Forest in Survival Data

Research on Mortality Risk of Chinese Older Adults from the Perspective of Social Health

Road Accessibility during Natural Hazards Based on Volunteered Geographic Information Data and Network Analysis

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