This paper presents part of the research in the field of human bioclimatology and refers to biothermal conditions in different geographical environments in Serbia: an urban area and a mountain of medium height. The goal of the paper was to show bioclimatic differences during the summer between the city of Belgrade (116 m a.s.l.) and the mountain resort of Zlatibor (1498 m a.s.l.). The basic principle of bioclimatic analysis is the human heat balance between man and environment. This methodological approach is a combination of physiological and meteorological parameters that result in thermophysiological bioclimatic indices: heat load (HL) in man and the Universal Thermal Climate Index (UTCI). For this analysis, weather data for July, as the warmest month, was obtained, using daily meteorological data for the decade from 2000 to 2010. Results for July indicate a considerable difference between the two abovementioned environments. HL in Belgrade was dominated by degrees of comfort "hot" and "extremely hot, with the highest value of 4.540, while for Zlatibor the dominant degree of comfort was "warm". The UTCI in Belgrade has dominated by strong heat stress and moderate heat stress, compared to Zlatibor where the UTCI is dominated by moderate heat stress. In addition, a significant part of the monitored decade on Mt. Zlatibor was without heat stress, with the exception of 2006 and 2007, indicating favorable biothermal characteristics. Therefore, compared to Belgrade, with its considerably lower overall heat stress Zlatibor has the characteristics of a site with favorable bioclimatic qualities.
The work examines the potential causative link between the flow of protons, i.e. temperature of the particles that are coming from the sun and forest fires in the USA. For determination of the degree of randomness for time series of input (temperature of protons) and output parameters (number of forest fires), the R/S analysis is conducted. The analysis of fractal dimension provides us an opportunity to compare self-similar processes in the influx of protons and the time series of forest fires flashes. Therefore we developed and conducted the sensitivity analysis of model based on hybrid neural networks ANFIS. As the calculations showed, only 16% of the real forest flashes cannot be predicted by the model.
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