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.
 

In the domain of spatial planning for border areas, it is imperative to adopt a macro-perspective view of geography to effectively identify the potentialities, capabilities, and limitations inherent to these regions. This approach aims to mitigate tensions and challenges arising from unbalanced development, deprivation, and spatial heterogeneity. The research methodology employed in this study is applied in purpose, utilizing a descriptive-analytical approach in terms of method. Specifically, a combination of multi-criteria decision-making techniques, including the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the Shannon entropy coefficient, has been utilized to evaluate the distribution and development of spatial development indicators within the health service sectors of Kurdistan Province. The border cities of this province have been classified according to their levels of these indicators. The central research question of this study is: What is the state of the cities in Kurdistan Province concerning the spatial distribution of health and service indicators? Preliminary findings suggest that the level of access to health services in each of the border cities in Kurdistan Province does not meet the desired standards. The results indicate that, despite its strategic geopolitical significance, Kurdistan Province remains one of the most deprived regions in the country. Furthermore, there exists a notable imbalance and heterogeneity in the distribution and development of facilities and indicators across the border cities of the province. An analysis of 13 indicators pertaining to spatial organization across four border cities reveals that Saqez and Baneh are classified within the semi-privileged (semi-deprived) group, while Marivan and Sarvabad fall within the deprived category. Consequently, the findings underscore that the spatial organization in the border cities of Kurdistan Province is markedly heterogeneous, positioning these cities at a middle to lower level in terms of health service development. It is essential to afford particular attention to this border province to ensure the enduring security and well-being of its citizens.