The primary goal of this paper was to investigate the strength of the influence of different types of risk on the travelers’ intention to visit destinations in future, that are, prejudiced due to COVID-19, marked as “stigmatized”, “isolated”, or “risky destinations”. Field interview research was conducted at the “Belgrade Nikola Tesla Airport” (Serbia). The results obtained by multiple regression analysis showed that all types of risks influenced the intention of travelers, with financial risk showing a more significant impact. Canonical discriminant analysis indicated that men were most afraid of human induced risk, service quality risk, natural disaster and COVID-19 risk, and they chose safer destinations. Among the women, the biggest fear was financial risk, socio-psychological risk, and food safety risk. Older respondents and those under the influence of external factors decided on safer destinations, while financial status did not play a significant role in predicting the choice of destination. The selection of the destination according to the degree of security was determined by the ordinal regression methodology. The entire research presents a certain novelty, because so far in the numerous studies on the topic of the negative consequences of COVID-19 on tourism, there has been no discussion of stigmatized or risky destinations that received that epithet, and were therefore negatively and unfairly marked in the minds of tourists for future visits.

The article deals with the complex dynamics of depopulation in rural areas in Serbia. Although the topic has been very topical in recent decades and research has long identified the causes and consequences of depopulation in numerous regions in Europe, there seems to be no systematic approach to the implementation of policies for the management of abandoned areas. We started from already known factors contributing to depopulation, including economic, social and environmental dimensions. Based on previous research, historical trends and a statistical database, we attempted to represent depopulation as a spatial process that has its own size, structure and consequences, using a new, quantitative methodology for grouping abandoned areas into clusters as basic units of dispersal of depopulated areas. The aim of the work was to quantitatively determine the size and structure of the depopulated area in Serbia after the 2022 census. The study opened up new fields of research for the future use of depopulated space, but also offered certain solutions for policy makers, researchers and municipalities struggling with the challenges of depopulation. The results of the work show the existence of 47 abandoned clusters in 10 administrative regions of Serbia, which is 17 more than in the 2011 census. Further homogenization and consolidation of clusters was found in the statistical regions: Toplička, Zaječarska, Jablanička, Pirotska and Raška region, i.e. an increase in the total depopulated area as well as its spatial expansion towards the north of the country.

Tourism is a significant segment of development policy at the national level, and it is important in terms of contributing to the Gross domestic product (GDP) diversifying of economy, reducing disparities between regions, fostering development capital, and promoting regional development. Tourism greatly contributes to the sustainable development of a region and a unit of local self-government, in an environment where sometimes there are no other alternatives for achieving that goal. Theories of regional development and tourism have evolved over a similar period after the Second World War. The concept of regional development has undergone a significant transformation through various theories. The paper analyzes the main principles of the theory of endogenous regional development and the possibility of its application for tourism research as a development factor. Tourism development policy is often created by the views of these theories. The purpose of this paper is to indicate, based on the analysis of modern ideas and approaches to regional development, whether they are suitable for the study of tourism and which factors are decisive in achieving its development function. The paper points out certain elements of theories that can individually or together represent the theoretical basis for tourism development planning in regions and local government units.

The impact of solar activity on environmental processes is difficult to understand and complex for empirical modeling. This study aimed to establish forecast models of the meteorological conditions in the forest fire areas based on the solar activity parameters applying the neural networks approach. During July and August 2018, severe forest fires simultaneously occurred in the State of California (USA), Portugal, and Greece. Air temperature and humidity data together with solar parameters (integral flux of solar protons, differential electron flux and proton flux, solar wind plasma parameters, and solar radio flux at 10.7 cm data) were used in long short-term memory (LSTM) recurrent neural network ensembles. It is found that solar activity mostly affects the humidity for two stations in California and Portugal (an increase in the integral flux of solar protons of > 30 MeV by 10% increases the humidity by 3.25%, 1.65%, and 1.57%, respectively). Furthermore, an increase in air temperature of 10% increases the humidity by 2.55%, 2.01%, and 0.26%, respectively. It is shown that temperature is less sensitive to changes in solar parameters but depends on previous conditions (previous increase of 10% increases the current temperature by 0.75%, 0.34%, and 0.33%, respectively). Humidity in Greece is mostly impacted by solar flux F10.7 cm and previous values of humidity. An increase in these factors by 10% will lead to a decrease in the humidity of 3.89% or an increase of 1.31%, while air temperature mostly depends on ion temperature. If this factor increases by 10%, it will lead to air temperature rising by 0.42%.

This research is devoted to the determination of hidden dependencies between the flow of particles that come from the Sun and precipitation-induced floods in the United Kingdom (UK). The analysis covers 20 flood events during the period from October 2001 to December 2019. The parameters of solar activity were used as model input data, while precipitations data in the period 10 days before and during each flood event were used as model output. The time lag of 0–9 days was taken into account in the research. Correlation analysis was conducted to determine the degree of randomness for the time series of input and output parameters. For establishing a potential causative link, machine learning classification predictive modeling was applied. Two approaches, the decision tree, and the random forest were used. We analyzed the accuracy of classification models forecast from 0 to 9 days in advance. It was found that the most important factors for flood forecasting are proton density with a time lag of 9, differential proton flux in the range of 310–580 keV, and ion temperature. Research in this paper has shown that the decision tree model is more accurate and adequate in predicting the appearance of precipitation-induced floods up to 9 days ahead with an accuracy of 91%. The results of this study confirmed that by increasing technical capabilities, using improved machine learning techniques and large data sets, it is possible to improve the understanding of the physical link between the solar wind and tropospheric weather and help improve severe weather forecasting.

The rural border areas of Serbia have been undergoing significant demographic shifts and transformations in land use. Between 2002 and 2022, these regions experienced a continuous population decline, an increase in the average age, and a growing share of single-person households. Simultaneously, there has been a reduction in agricultural land and a noticeable expansion of forested and grassland areas, particularly in hilly and mountainous terrain. This paper aims to explore the interrelationship between demographic indicators and land cover changes in these areas. Pearson’s correlation analysis was applied to data from the national population censuses and the CORINE Land Cover datasets for 1990 and 2018. The strongest positive correlation was found between the decline in the number of households and the reduction in agricultural land. Conversely, the expansion of forested areas showed a negative correlation with most demographic indicators. The findings reflect trends similar to those observed in other Eastern European countries but also reveal specific patterns of spatial marginalization unique to Serbia. In the study, the conclusion leads to the idea that depopulated border areas are in transition between past and future functions that will be influenced by their resource base.