基于BP 神经网络的我国石化类上市公司经营风险预警系统&lt;br&gt;Early Warning System of Operational Risks in Petrochemical Listed Companies in China Based on BP Artificial Neural Network

In some situations, there is no possibility of hazard mitigation, especially if the hazard is induced by water. Thus, it is important to prevent consequences via an early warning system (EWS) to announce the possible occurrence of a hazard. The aim and objective of this paper are to investigate the possibility of implementing an EWS in a small-scale catchment and to develop a methodology for developing a hydrological prediction model based on an artificial neural network (ANN) as an essential part of the EWS. The methodology is implemented in the case study of the Slani Potok catchment, which is historically recognized as a hazard-prone area, by establishing continuous monitoring of meteorological and hydrological parameters to collect data for the training, validation, and evaluation of the prediction capabilities of the ANN model. The model is validated and evaluated by visual and common calculation approaches and a new evaluation for the assessment. This new evaluation is proposed based on the separation of the observed data into classes based on the mean data value and the percentages of classes above or below the mean data value as well as on the performance of the mean absolute error.

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Developing Strategies in the Sharing Economy

10.4018/978-1-6684-2408-7.ch059 ◽

2022 ◽

pp. 1224-1245

Author(s):

Ramona Diana Leon

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Early Warning ◽

Early Warning System ◽

Business Models ◽

Warning System ◽

Sharing Economy ◽

Knowledge Based ◽

Artificial Neural ◽

The One

The sharing economy is challenging the traditional business models and strategies by encouraging collaboration, non-ownership, temporal access, and redistribution of goods and/or services. Within this framework, the current chapter aims to examine how managers influence, voluntarily or involuntarily, the reliability of a managerial early warning system, based on an artificial neural network. The analysis focuses on seven Romanian sustainable knowledge-based organizations and brings forward that managers tend to influence the results provided by a managerial early warning system based on artificial neural network, voluntarily and involuntarily. On the one hand, they are the ones who consciously decide which departments and persons are involved in establishing the structure of the managerial early warning system. On the other hand, they unconsciously influence the structure of the managerial early warning system through the authority they exercise during the managerial debate.

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Developing Strategies in the Sharing Economy

Strategies for Business Sustainability in a Collaborative Economy - Practice, Progress, and Proficiency in Sustainability ◽

10.4018/978-1-7998-4543-0.ch011 ◽

2020 ◽

pp. 199-221

Author(s):

Ramona Diana Leon

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Early Warning ◽

Early Warning System ◽

Business Models ◽

Warning System ◽

Sharing Economy ◽

Knowledge Based ◽

Artificial Neural ◽

The One

The sharing economy is challenging the traditional business models and strategies by encouraging collaboration, non-ownership, temporal access, and redistribution of goods and/or services. Within this framework, the current chapter aims to examine how managers influence, voluntarily or involuntarily, the reliability of a managerial early warning system, based on an artificial neural network. The analysis focuses on seven Romanian sustainable knowledge-based organizations and brings forward that managers tend to influence the results provided by a managerial early warning system based on artificial neural network, voluntarily and involuntarily. On the one hand, they are the ones who consciously decide which departments and persons are involved in establishing the structure of the managerial early warning system. On the other hand, they unconsciously influence the structure of the managerial early warning system through the authority they exercise during the managerial debate.

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Artificial Neural Network Intelligent System on the Early Warning System of Landslide

Pertanika Journal of Science and Technology ◽

10.47836/pjst.29.2.13 ◽

2021 ◽

Vol 29 (2) ◽

Author(s):

Aghus Sofwan ◽

Sumardi ◽

Najib ◽

Indrah Wendah Atma Bhirawa

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Soil Moisture ◽

Early Warning ◽

Early Warning System ◽

Intelligent System ◽

Warning System ◽

Ground Movement ◽

Sloping Ground ◽

Artificial Neural

Landslide is a natural sloping ground movement disaster that can occur due to several factors such as high rainfall, soil moisture in the depth of the soil of an area, vibrations experienced in the region, and the slope of the ground structure. A system that can deliver these factor values into the levels of vulnerability of landslide disasters is needed. The system uses Arduino Mega 2560 to process the level of vulnerability. It can predict the moment and the probability of the disaster occurring as an early warning system. The artificial neural network (ANN) intelligent system can expect an event of a disaster. The designed ANN used five parameters causing landslide as input data: rainfall, slope, soil moisture on the surface, soil moisture in the ground’s depth, and soil vibration. The ANN system output delivered three-level conditions: the safe, the standby, and the hazardous. The feed-forward backpropagation (FFBP) and the cascade forward backpropagation (CFBP) methods were analyzed. The performance of both methods was compared in terms of minimum square error (MSE). The MSE results of FFBP and CFBP in the safe, the standby, and the hazardous conditions were 0.017076 and 0.034952; 0.049597 and 0.046764; 0.062105 and 0.060355; respectively. The results point to the supremacy of CFBP to FFBP in standby and hazardous conditions. Therefore, the CFBP is implemented into the hardware of the early warning system.

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