APPLICATION OF TYPE-2 FUZZY ALGORITHMS IN TOURISM SERVICE SUCCESS PREDICTION

Abstract

This research introduces a novel approach for forecasting the success of new tourism services by employing a Type-2 Fuzzy algorithm optimized through the Particle Swarm Optimization (PSO) method. Interval Type-2 Fuzzy sets have been utilized to manage uncertainties more effectively. While existing studies have employed a range of techniques, including model-based approaches, to predict the success of a new service, this study integrates the Type-2 Fuzzy algorithm with other computational techniques to form a hybrid system. This integration enhances the predictive accuracy over that of individual methods. The data for this analysis was derived from a survey that examined the variables that were deemed critical to launching a new tourism service. A selection method was applied to cycle through all possible inputs in order to identify the two most significant ones. The final model was trained using a subset of the collected data and validated with the remainder. The performance of the Type-2 Fuzzy algorithm was evaluated using well-established metrics. The results demonstrate that the Type-2 Fuzzy algorithm effectively manages the uncertainties inherent in the input-output relationships, establishing it as a dependable method for predicting the success of new tourism services. Comparative analysis indicates that the proposed method outperforms existing techniques