Development of Early Warning Model for Financial Firms Using Financial and Text Data : A Case Study on Insolvent Bank Prediction

To reduce the occurrence of ship collisions, immediate danger, and close-quarters situations in narrow inland waterways, a step-by-step early warning system for ship collision-avoidance actions was developed, along with an early warning method and model of collision risk based on coordinated collision-avoidance actions. This study first analyzed the importance of coordinated collision-avoidance actions in inland waterways, and the process and key components of coordinated collision-avoidance actions were studied. Then, the early warning method of inland ship collision risk based on coordinated collision-avoidance actions was introduced; the effectiveness of the early warning method was comparatively analyzed via experimental observations. A framework of early warning model of inland ship collision risk was created based on the early warning method; a collision risk early warning model for inland ships based on coordinated collision-avoidance actions was proposed according to the relationship between the distance/time to the closest point of approach (DCPA, TCPA), coordination degree of collision-avoidance actions of the two considered ships and collision risk; moreover, the early warning model of inland ship collision risk was further considered for quantitative calculation. Finally, the application of the early warning method and model was demonstrated using a case study. The results indicate that the early warning method of inland ship collision risk based on coordinated collision-avoidance actions could effectively reduce the emergence of close-quarters situations and immediate danger, and the early warning model could quantitatively show the evolution of collision risk of two ships along with the process of coordinated collision-avoidance actions.

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Construction of Influenza Early Warning Model Based on Combinatorial Judgment Classifier: A Case Study of Seasonal Influenza in Hong Kong

Current Medical Science ◽

10.1007/s11596-021-2493-0 ◽

2022 ◽

Author(s):

Zi-xiao Wang ◽

James Ntambara ◽

Yan Lu ◽

Wei Dai ◽

Rui-jun Meng ◽

...

Keyword(s):

Hong Kong ◽

Early Warning ◽

Seasonal Influenza ◽

Early Warning Model ◽

Model Based ◽

Warning Model

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Establishment and Validation of Early-warning Model of Perioperative Pain for the Elders

Case Medical Research ◽

10.31525/ct1-nct04228705 ◽

2020 ◽

Author(s):

Keyword(s):

Early Warning ◽

Early Warning Model ◽

Perioperative Pain ◽

Warning Model

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The Level of Public Acceptance to the Development of a Coastal Flooding Early Warning System in Jakarta

Sustainability ◽

10.3390/su13020566 ◽

2021 ◽

Vol 13 (2) ◽

pp. 566

Author(s):

Nelly Florida Riama ◽

Riri Fitri Sari ◽

Henita Rahmayanti ◽

Widada Sulistya ◽

Mohamad Husein Nurrahmat

Keyword(s):

Path Analysis ◽

Early Warning ◽

Early Warning System ◽

Warning System ◽

Coastal Flooding ◽

Coastal Areas ◽

Public Acceptance ◽

Community Attitudes ◽

Early Warning Model ◽

Warning Model

Coastal flooding is a natural disaster that often occurs in coastal areas. Jakarta is an example of a location that is highly vulnerable to coastal flooding. Coastal flooding can result in economic and human life losses. Thus, there is a need for a coastal flooding early warning system in vulnerable locations to reduce the threat to the community and strengthen its resilience to coastal flooding disasters. This study aimed to measure the level of public acceptance toward the development of a coastal flooding early warning system of people who live in a coastal region in Jakarta. This knowledge is essential to ensure that the early warning system can be implemented successfully. A survey was conducted by distributing questionnaires to people in the coastal areas of Jakarta. The questionnaire results were analyzed using cross-tabulation and path analysis based on the variables of knowledge, perceptions, and community attitudes towards the development of a coastal flooding early warning system. The survey result shows that the level of public acceptance is excellent, as proven by the average score of the respondents’ attitude by 4.15 in agreeing with the establishment of an early warning system to manage coastal flooding. Thus, path analysis shows that knowledge and perception have a weak relationship with community attitudes when responding to the coastal flooding early warning model. The results show that only 23% of the community’s responses toward the coastal flooding early warning model can be explained by the community’s knowledge and perceptions. This research is expected to be useful in implementing a coastal flooding early warning system by considering the level of public acceptance.

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Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network

Symmetry ◽

10.3390/sym13061082 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1082

Author(s):

Fanqiang Meng

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Early Warning ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Coal Face ◽

Early Warning Model ◽

Working Face ◽

The Neural Network ◽

Warning Model

Risk and security are two symmetric descriptions of the uncertainty of the same system. If the risk early warning is carried out in time, the security capability of the system can be improved. A safety early warning model based on fuzzy c-means clustering (FCM) and back-propagation neural network was established, and a genetic algorithm was introduced to optimize the connection weight and other properties of the neural network, so as to construct the safety early warning system of coal mining face. The system was applied in a coal face in Shandong, China, with 46 groups of data as samples. Firstly, the original data were clustered by FCM, the input space was fuzzy divided, and the samples were clustered into three categories. Then, the clustered data was used as the input of the neural network for training and prediction. The back-propagation neural network and genetic algorithm optimization neural network were trained and verified many times. The results show that the early warning model can realize the prediction and early warning of the safety condition of the working face, and the performance of the neural network model optimized by genetic algorithm is better than the traditional back-propagation artificial neural network model, with higher prediction accuracy and convergence speed. The established early warning model and method can provide reference and basis for the prediction, early warning and risk management of coal mine production safety, so as to discover the hidden danger of working face accident as soon as possible, eliminate the hidden danger in time and reduce the accident probability to the maximum extent.

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