A Research on the Fuzzy Risk Assessment Model of Storm Surge Disaster Based on Artificial Neural Network

2010 ◽  
Author(s):  
Fang Ji ◽  
Yijun Hou
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Artificial Neural Network ◽  
Storm Surge ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Artificial Neural ◽  
Storm Surge Disaster ◽  
Fuzzy Risk Assessment

The Application of Fuzzy Analytic Hierarchy Process Method and Artificial Neural Network Model in the Elevator Risk Assessment

2014 ◽  
Vol 1014 ◽  
pp. 552-555
Author(s):  
Xin Shi Li ◽  
You Cai Xu ◽  
Ran Tao ◽  
Shu Guo ◽  
Kun Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Artificial Neural Network ◽  
Analytic Hierarchy Process ◽  
Assessment Model ◽  
Fuzzy Analytic Hierarchy Process ◽  
Risk Assessment Model ◽  
Analytic Hierarchy ◽  
Artificial Neural ◽  
Hierarchy Process

The tradition elevator risk assessment model depends on the expert experience, which causes that the assessment process takes a long time. To deal with this problem, this paper proposes a new risk assessment model which is based on fuzzy analytic hierarchy process (F-AHP) and artificial neural network (ANN). This model is applied to the risk-assessment of elevators. The results show that the assessment time is shorter and the accuracy is not lower, in comparison with the traditional model.

scholarly journals Fine-Scale Coastal Storm Surge Disaster Vulnerability and Risk Assessment Model: A Case Study of Laizhou Bay, China

2020 ◽  
Vol 12 (8) ◽  
pp. 1301 ◽  
Author(s):  
Yueming Liu ◽  
Chen Lu ◽  
Xiaomei Yang ◽  
Zhihua Wang ◽  
Bin Liu
Keyword(s):  
Risk Assessment ◽  
Storm Surge ◽  
Storm Surges ◽  
Assessment Model ◽  
Storm Water ◽  
Laizhou Bay ◽  
Risk Assessment Model ◽  
Fine Scale ◽  
Disaster Vulnerability ◽  
Storm Surge Disaster

In the assessment of storm surge vulnerability, existing studies have often selected several types of disaster-bearing bodies and assessed their exposure. In reality, however, storm surges impact all types of disaster-bearing bodies in coastal and estuarine areas. Therefore, all types of disaster-bearing bodies exposed to storm surges should be considered when assessing exposure. In addition, geographical factors will also have an impact on the exposure of the affected bodies, and thus need to be fully considered. Hence, we propose a fine-scale coastal storm surge disaster vulnerability and risk assessment model. First, fine-scale land-use data were obtained based on high-resolution remote sensing images. Combined with natural geographic factors, such as the digital elevation model (DEM), slope, and distance to water, the exposure of the disaster-bearing bodies in each geographic unit of the coastal zone was comprehensively determined. A total of five indicators, such as the percentage of females and ratio of fishery products to the gross domestic product (GDP), were then selected to assess sensitivity. In addition, six indicators, including GDP and general public budget expenditure, were selected to assess adaptability. Utilizing the indicators constructed from exposure, sensitivity, and adaptability, a vulnerability assessment was performed in the coastal area of Laizhou Bay, China, which is at high risk from storm surges. Furthermore, the storm surge risk assessment was achieved in combination with storm water statistics. The results revealed that the Kenli District, Changyi City, and the Hanting District have a higher risk of storm surge and require more attention during storm surges. The storm surge vulnerability and risk assessment model proposed in this experiment fully considers the impact of the natural environment on the exposure indicators of the coastal zone’s disaster-bearing bodies, and combines sensitivity, adaptability indicators, and storm water record data to conduct vulnerability and risk assessment. At the same time, the model proposed in this study can also realize multi-scale assessment of storm surge vulnerability and risk based on different scales of socioeconomic statistical data, which has the advantages of flexibility and ease of operation.

scholarly journals Fuzzy risk assessment of a deeply buried tunnel under incomplete information

2018 ◽  
Vol 5 (10) ◽  
pp. 180305 ◽  
Author(s):  
Yuanpu Xia ◽  
Ziming Xiong ◽  
Hao Lu ◽  
Zhu Wen ◽  
Chao Ma
Keyword(s):  
Risk Assessment ◽  
Similarity Measure ◽  
Fuzzy Set ◽  
Risk Control ◽  
Assessment Model ◽  
Design Stage ◽  
Tunnel Construction ◽  
Risk Assessment Model ◽  
Control Effect ◽  
Fuzzy Risk Assessment

Risk assessment has always been an important part of safety risk research in tunnel and underground engineering. Owing to the characteristics of tunnel construction, to achieve an expected risk control effect, it is necessary to carry out accurate risk assessment research according to the risk assessment concept based on the entire tunnel construction process. At present, because of the frequent occurrences of safety accidents, a variety of risk assessment models have been proposed for different tunnel projects such as subways and railway tunnels, which can be roughly classified into two types: probability-based and fuzzy set theories. However, the existing models may be more suitable for the construction stage, and the design stage lacks a reliable and practical fuzzy risk assessment method. Therefore, based on fuzzy set theory and similarity measure theory, a risk assessment model is proposed to adapt to the characteristics that the risk information is difficult to quantify the fuzziness in the design phase. Firstly, new ideas of fuzzy risk analysis are proposed to overcome deficiencies in existing methods; secondly, a new similarity measure is constructed; then fusing multi-source fuzzy information based on evidence theory, the relationship between similarity measure and mass function is established. Finally, the new method is applied to the Yuelongmen tunnel. Results show that the concept of risk control and the risk assessment model are feasible.

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