Title
Development of Adaptive Neuro-Fuzzy Inference System for Assessing Industry Leadership in Accident Situations
Abstract
Petroleum activity is characterized as a high-risk activity due to the probability of accidents with material and human losses. The leaders of this segment assume, besides the complex routine tasks, the challenge of making assertive decisions during an accident. This study aims to present an evaluation model of the Industry Leadership Index for Emergencies Situations (ILIE), using the Adaptive Neuro-Fuzzy System (ANFIS). The model was composed of 4 input variables, namely: knowledge, behavior, skill, and attitude; and one output variable, Industry Leadership. The data collection took place in petroleum production units in Brazil, with a sample of 151 respondents through the application of a survey. The observed data were treated in an Excel tabulator and used in the development of the ANFIS model. From this model, it was possible to carry out simulations to predict the impact, which the increase or decrease in the value of each input variable can influence the leader's profile. The model performed satisfactorily in the Root of the Mean Square Error (RMSE) analysis, being 0.199 in data training and 1.217 in data verification. The results suggest that the ANFIS method can be successfully applied to establish a model to analyze industry leaders prepared for assertive responses in crisis scenarios.
Year
DOI
Venue
2022
10.1109/ACCESS.2022.3206766
IEEE ACCESS
Keywords
DocType
Volume
Leadership, Industries, Behavioral sciences, Accidents, Safety, Petroleum, Customer relationship management, Input variables, Adaptation models, Emergency services, Fuzzy neural networks, Leadership, industry, evaluation, emergency, ANFIS
Journal
10
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
5