A Novel Prediction of Tourism Economic Development Trends based on Machine Learning

Tourism economic forecasting is a critical exercise hindered by cyclical variations, unforeseen factors, and multiple-variable interdependence. Conventional methods like regression and ARIMA are inadequate in capturing non-linear relationships and massive data streams. This study presents a prediction model based on neural networks that identifies intricate correlations among variables such as tourist arrival, seasonal patterns, consumption, and investment in infrastructure. A feedforward neural network with multiple layers is trained on past tourism data, optimized by backpropagation and data normalization. The output of the model predicts future growth rates and revenue of tourism. Accuracy and Precision score are used for performance benchmarking. The neural network provides 97.2% greater accuracy than conventional models. This facilitates informed decision-making by policymakers and tourism boards for resource allocation, advertisement campaigns, and infrastructure development. The solution presents a useful instrument for tourism economics strategic planning.