A Machine Learning-Based Ecological Impact Assessment Model for Tourism Destinations

Conservation efforts may find tourism either helpful or harmful. Protected environments felt the environmental toll of tourism’s growth and variety. A framework for environmentally responsible tourism was proposed based on real-world studies that examined the connection between growth in tourism and environmental compatibility. Global interest in the Ecological Impact Assessment (EIA) of tourist attraction issues has grown as environmental preservation has gained more and more attention. The paradigm proposed a moderating role for government support and policy interventions in preserving an ecological system while striking a balance between corporate and ecological concerns. The research population includes all parties involved in tourism, such as travelers, local government officials, hotel owners, and tour operators that operate in the region. This study aims to develop an integrated decision-making method for environmental impact assessments of tourist attractions. A model for predicting the demand for cruise tourism called IRBFNN-GSA, which is an improved radial basis function neural network with a gravitational search algorithm, and a model for predicting the effect of tourism on changes in the density of vegetation on land, called Support Vector Machine (SVM), are both suggested to improve the effectiveness of forecasts. An improved radial basis function neural networks model’s hyper-parameters are fine-tuned using GSA in the suggested variant. The results show that IRBFNN-GSA has the best predicting performance when used with certain mobile keywords and economic indices when compared with other models with different parameters. The findings show that SVMs are useful predictors for tourist demand forecasting and that the methodology’s recommended framework is successful.