Sustainable planning of developing tourism destinations after COVID-19 outbreak: a deep learning approach

Tourist destinations across the globe have been hit by the worst of the crisis that ensued the Covid-19 pandemic, and this concern is exponentially worse in developing countries. Sustainable planning of these countries to face the unheralded crises demands an approach to provide the most efficient solutions using past experiences, unique characteristics of the present crisis, and existing obstacles and challenges. The present study was conducted to develop a Deep Neural Network (DNN) model using experiences of different countries in beating the crises in the tourism industry. Relying on its generalization capability, the proposed model can forecast the 'sustainable effectiveness of possible policies' in developing countries by modelling the dynamics between the characteristics of these systems and the possible policies. A case study of a developing country was conducted to explain the model development process and its efficiency. Based on the data of the characteristics of the Covid-19 crisis and the tourism industry under study, the model outputs indicated that the most effective and sustainable policy to resume the pre-crisis conditions is to employ the combined policy of focusing on domestic tourism and crisis preparedness.