Evaluation of Ice and Snow Tourism Competitiveness based on Artificial Neural Networks

Assessing competitiveness of ice and snow tourism destinations is a three-dimensional issue consisting of environmental, economic, and service-related considerations. Conventional assessment techniques fail to handle a variety of data types and tend to produce biased ranking outcomes. This study employs an Artificial Neural Network (ANN) to assess tourism competitiveness by analyzing relationships between indicators such as transport, lodging, recreational facilities, customer satisfaction, and seasonal factors. The ANN acquires the weighted impact of every factor on competitiveness through a three-layer architecture. Data is sourced from government tourism databases and surveys. Validation metrics including Accuracy, precision, and F1score are utilized. The model attains a 98.7 % accuracy in aligning with expert ratings. It facilitates regional tourism authorities to identify areas of improvement and investment. This smart assessment approach facilitates sustainable development as well as focused marketing of winter tourism.