Risk Assessment Model Based on SVDD and Fuzzy Regression Method

2011 ◽  
Vol 58-60 ◽  
pp. 1807-1812
Author(s):  
Hui Li Zhou ◽  
Wen Bing Chang ◽  
Sheng Han Zhou
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
Assessment Model ◽  
Fuzzy Regression ◽  
Risk Level ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Risk Assessment Model ◽  
Evaluation Indexes ◽  
Model Based

The paper aims to solve the problem of insufficient high risk data in risk assessment of R&D projects. A one-class classification method called support vector data description (SVDD) is studied, and an intelligent risk assessment model based on SVDD with fuzzy regression information is also proposed. The model comes into being a new approach. Applying this approach, firstly verify the conversional risk evaluation indexes by fuzzy regression technique to develop a sensitive index system. Secondly the study uses the historical risk data referring to these indexes to train the SVDD one-class classifier. Unlike previously proposed intelligent methods of risk assessment, with this model the risk level can be distinguished only by training of low risk data. The results of its application on an example show that the method is feasible for risk assessment with the fuzzy high risk data.

Workplace Safety Risk Assessment Model Based on Fuzzy Regression

2018 ◽  
Vol 24 (3) ◽  
pp. 1656-1659 ◽  
Author(s):  
Nureize Arbaiy ◽  
Hamijah Ab Rahman ◽  
Mohd Zaki Mohd Salikon ◽  
Pei Chun Lin
Keyword(s):  
Risk Assessment ◽  
Workplace Safety ◽  
Assessment Model ◽  
Fuzzy Regression ◽  
Risk Assessment Model ◽  
Safety Risk ◽  
Model Based

An assessment model of monitoring risk in deep excavation based on fuzzy theory

2019 ◽  
Vol 29 (2) ◽  
pp. 221-229
Author(s):  
Lin Long ◽  
Zhida Li
Keyword(s):  
Risk Assessment ◽  
Risk Evaluation ◽  
Fuzzy Theory ◽  
Assessment Model ◽  
Engineering Practice ◽  
Risk Assessment Model ◽  
Evaluation Function ◽  
Deep Excavation ◽  
Evaluation Indexes ◽  
Model Based

In order to better evaluate the monitoring risk of deep excavation, an assessment model based on fuzzy theory was established by combining the uncertainty and membership degree of evaluation indexes with the fuzziness of experts’ comments. Firstly, considering the risk sources as evaluation indexes, the monitoring risk evaluation system for deep excavation was establish, and the risk evaluation function was constructed. Secondly, the risk assessment model based on fuzzy theory was put forward, analytic hierarchy process was used to determine the weight of evaluation indexes, and expert evaluation weight was introduced to correct the evaluation function. Finally, comprehensive risk grade in deep excavation was obtained and the risk decisions were put forward by calculating the value of modified evaluation function. Through the application of the monitoring risk assessment model in the deep excavation of World Trade Group, the results are in accordance with the engineering practice. The results suggest that the model based on fuzzy theory is efficient for evaluating the monitoring risks and can be used in the application of deep excavation evaluation. Meanwhile, the applicability and accuracy of the assessment model are verified.

scholarly journals An Alternative Risk Assessment Model of Urban Waterlogging: A Case Study of Ningbo City

2021 ◽  
Vol 13 (2) ◽  
pp. 826
Author(s):  
Meiling Zhou ◽  
Xiuli Feng ◽  
Kaikai Liu ◽  
Chi Zhang ◽  
Lijian Xie ◽  
...  
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
Assessment Model ◽  
Risk Assessments ◽  
Risk Assessment Model ◽  
Risk Area ◽  
The North ◽  
Coastal Cities ◽  
Risk Areas ◽  
The City

Influenced by climate change, extreme weather events occur frequently, and bring huge impacts to urban areas, including urban waterlogging. Conducting risk assessments of urban waterlogging is a critical step to diagnose problems, improve infrastructure and achieve sustainable development facing extreme weathers. This study takes Ningbo, a typical coastal city in the Yangtze River Delta, as an example to conduct a risk assessment of urban waterlogging with high-resolution remote sensing images and high-precision digital elevation models to further analyze the spatial distribution characteristics of waterlogging risk. Results indicate that waterlogging risk in the city proper of Ningbo is mainly low risk, accounting for 36.9%. The higher-risk and medium-risk areas have the same proportions, accounting for 18.7%. They are followed by the lower-risk and high-risk areas, accounting for 15.5% and 9.6%, respectively. In terms of space, waterlogging risk in the city proper of Ningbo is high in the south and low in the north. The high-risk area is mainly located to the west of Jiangdong district and the middle of Haishu district. The low-risk area is mainly distributed in the north of Jiangbei district. These results are consistent with the historical situation of waterlogging in Ningbo, which prove the effectiveness of the risk assessment model and provide an important reference for the government to prevent and mitigate waterlogging. The optimized risk assessment model is also of importance for waterlogging risk assessments in coastal cities. Based on this model, the waterlogging risk of coastal cities can be quickly assessed, combining with local characteristics, which will help improve the city’s capability of responding to waterlogging disasters and reduce socio-economic loss.

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