Today, due to the conditions of globalization, city branding has become increasingly important due to its tourism potentials. Most regions with strategic planning in this area aim to achieve economic development and reduce deprivation through tourism. In this study, the research strategy is deductive, and its purpose is practical. The required data and information were collected from library resources and surveys (questionnaires and interviews) with experts. The meta-SWOT technique, based on an inside-out approach and a resource-based perspective, provides a framework for enhancing the competitiveness of cities and regions. The city of Kermanshah possesses significant capabilities to attract tourism, enabling it to achieve urban, regional, and global competitiveness. The findings of this study show that among the capabilities and potentials of Kermanshah, the existence of historical and cultural monuments—such as Taq-e Bostan, pillars, historic houses, museums, and its role as a center for handicrafts—as well as cultural similarities with people living in Iraq and Turkey, along with its unique culture, beliefs, and customs, serve as key tourism potentials. These attributes align with the four characteristics of the VIRO framework (Value, Rarity, Imitability, and Organization), making them the most strategic fit with macro variables affecting tourist attraction and urban competitiveness through tourism in Kermanshah. Among the major influential variables, the COVID-19 pandemic, international sanctions against Iran, climate change, natural disasters, and lack of funding have the greatest impact on the urban competitiveness of Kermanshah. Based on the analysis of resources and macro variables, a strategic fit map was developed, and appropriate solutions were proposed.

Dust is a phenomenon with significant environmental impacts across various aspects of human life, including agriculture, economy, health, and more. The purpose of this study is to investigate and predict the dust phenomenon in Kermanshah. Meteorological data with a 3-hour resolution for the statistical period (2000–2020) from the Kermanshah station was obtained from the Meteorological Organization. First, the dust data were normalized, and then Artificial Neural Network (ANN) models were used to predict dust concentration, while the Adaptive Neuro-Fuzzy Inference System (ANFIS) was employed to analyze and predict the time series of dust occurrence in MATLAB software. The findings revealed that the maximum predicted dust concentration, related to the minimum dew point with the highest Pearson correlation with dust, was estimated at 3451.23 µg/m³. Additionally, the results of the time series prediction using the ANFIS model showed that the linear bell membership function with grade 3, during both the training and testing stages, was the most effective input function among other membership functions. According to the forecasting models, the highest probability of maximum dust occurrence in the next 20 years in Kermanshah is 94%. Based on the aforementioned studies, sufficient information was gathered to conduct this research. The phenomenon of dust, particularly in western Iran and the city of Kermanshah, has consistently posed significant challenges for the residents of these areas. This phenomenon is influenced by specific atmospheric conditions that cause irreparable damage annually, leading to respiratory issues and deteriorating air quality. Therefore, it is essential to pay serious attention to the issue of dust.