Applying machine learning in the investigation of the link between the high-velocity streams of charged solar particles and precipitation-induced floods.

This study explores a possible link between solar activity and floods caused by precipitation. For this purpose, discrete blocks of data for 89 separate flood events in Europe in the period 2009-2018 were used. Solar activity parameters with a time lag of 0-11 days were used as input data of the model, while precipitation data in the 12 days preceding the flood were used as output data. The level of randomness of the input and output time series was determined by correlation analysis, while the potential causal relationship was established by applying machine learning classification predictive modeling. A total of 25 distinct machine-learning algorithms and four model ensembles were applied. It was shown that in 81% of cases, the designed model could explain the occurrence or absence of precipitation-induced floods 9 days in advance. Differential proton flux in the 0.068-0.115 MeV and integral proton flux > 2.5 MeV were found to be the most important factors for forecasting precipitation-induced floods. The study confirmed that machine learning is a valuable technique for establishing nonlinear relationships between solar activity parameters and the onset of floods induced by precipitation.

Evaluation of outdoor thermal comfort conditions: evidence from the Serbian major ski resort over the last 30 years.

The study aims to present reliable information about thermal conditions and their impacts on visitors to ski travel destinations. Mountain tourism areas are specific since high altitudes affect the ambient weather conditions which can affect different types of human activities. In this paper, the thermal comfort and its changes in Kopaonik Mountain, the most popular ski resort in Serbia over the last 30 years, have been evaluated. Information about thermal comfort is presented by using the Universal Thermal Climate Index (UTCI), physiologically equivalent temperature (PET), and modified physiologically equivalent temperature (mPET) in 3-h resolution for the period 1991-2020. The results indicate prevailing cold stress all year round. Days with moderate, strong, and very strong heat stress were not recorded. Strong and extreme cold stress prevailed during winter, while slight and moderate cold stress prevailed during summer. Transitional seasons were very cold, but autumn was more comfortable than spring. The occurrence of days with neutral and slightly warm/cool conditions is concentrated in the summer months. However, summer is not used enough for tourism because the choice of tourists to stay at Kopaonik is not primarily based on favorable bioclimatic conditions, but on resources for winter tourism. With global warming, the annual number of thermally favorable days has been increasing, while the number of days with extreme and strong cold stress is decreasing. Continuing this trend can significantly influence tourism in the future, and therefore, new strategies in ski resorts will be required to adapt to the changing climate.

COVID-19 certificate as a cutting-edge issue in changing the perception of restaurants' visitors-Illustrations from Serbian urban centers.

As one of the first European cases of the introduction of COVID-19 certificates, the Serbian Government initiated the measure of limited working hours of restaurants for unvaccinated visitors. Due to such actions and frequent bans on working during the pandemic, many restaurants in Serbia had to lay off workers or close. At the end of October 2021, the certificate for entering restaurants and all catering facilities for all the visitors became mandatory. It is interesting to note that earlier findings suggested that some personality characteristics determine the specific behaviors during the pandemic, but there is still a small number of results related to restaurants' visitors. This study aimed to investigate the predictive strength of the Big Five Factors (BFF) to attitudes toward visits to restaurants in Serbia during the pandemic, depending on the attitudes toward accepting COVID-19 certificates. A survey was conducted on a total sample of 953 visitors of restaurants in three major cities in Serbia. The results of hierarchical regression analysis showed that Openness and Extraversion positively predict attitudes toward visits to facilities during a pandemic, while Conscientiousness and Neuroticism were negative predictors. However, in the second step of hierarchical regression analysis, attitudes toward a COVID-19 certificate as a mediator variable significantly reduced the negative effect of Neuroticism on the attitudes toward visits. It seems that, by obtaining the certificate, the fear of unsafe stays in restaurants can be reduced, and that making decisions about (no) visiting restaurants during the pandemic does not necessarily have to be compromised by emotional lability.

Health risks of extended exposure to low-level UV radiation - An analysis of ground-based and satellite-derived data.

This study aims to indicate the importance of revising current health recommendations concerning the duration of exposure and individual sensitivity of the skin to solar ultraviolet (UV) radiation. For this purpose, a 16-year data series (2005-2020) of erythemal radiant exposure (Her) and UV index (UVI) for Serbia was analyzed. The UV-related risk was estimated for lighter skin (skin phototypes I-IV) under prolonged exposure on days when maximum UVI was below the recommended protection threshold (UVIlow days, for UVI < 3). Risk assessment was performed for seasonal exposure using satellite-derived data (OMUVBd product) previously validated by ground-based measurements in Novi Sad. The assessment of harmful effects included an analysis of the relation between the daily maximum UVI and the corresponding daily Her, the occurrence of UVIlow days, the exceedance of minimal erythema dose (MED), and the minimum duration of exposure to induce erythema (tMED) for all lighter skin phototypes. It was found that the share of UVIlow days in the total number of days in Serbia increases with the latitude, with the highest percentage in winter (up to 69.454%) and the lowest in summer (up to 3.468%). The results show that the daily Her frequently exceeded the harmful threshold for lighter skin phototypes I-IV (on average by 91.521, 84.923, 70.556, and 56.515%, respectively) on UVIlow days. It was found that prolonged exposure on days with a maximum of UVI = 2 poses a significant risk of erythema for all lighter skin phototypes, even for a duration of 3 h in the middle of the day, as well as medium risk for UVI = 1, and an absence of risk for UVI = 0. The results suggest that health recommendations should be revised, especially in the mid-latitudes, where the share of UVIlow days is large, and in areas where the population is predominantly lighter-skinned.

Prediction of tropospheric ozone concentration using artificial neural networks at traffic and background urban locations in Novi Sad, Serbia.

In this paper, we described generation and performances of feedforward neural network models that could be used for a day ahead predictions of the daily maximum 1-h ozone concentration (1hO3) and 8-h average ozone concentration (8hO3) at one traffic and one background station in the urban area of Novi Sad, Serbia. The six meteorological variables for the day preceding the forecast and forecast day, ozone concentrations in the day preceding the forecast, the number of the day of the year, and the number of the weekday for which ozone prediction was performed were utilized as inputs. The three-layer perceptron neural network models with the best performance were chosen by testing with different numbers of neurons in the hidden layer and different activation functions. The mean bias error, mean absolute error, root mean squared error, correlation coefficient, and index of agreement or Willmott's Index for the validation data for 1hO3 forecasting were 0.005 µg m-3, 12.149 µg m-3, 15.926 µg m-3, 0.988, and 0.950, respectively, for the traffic station (Dnevnik), and - 0.565 µg m-3, 10.101 µg m-3, 12.962 µg m-3, 0.911, and 0.953, respectively, for the background station (Liman). For 8hO3 forecasting, statistical indicators were - 1.126 µg m-3, 10.614 µg m-3, 12.962 µg m-3, 0.910, and 0.948 respectively for the station Dnevnik and - 0.001 µg m-3, 8.574 µg m-3, 10.741 µg m-3, 0.936, and 0.966, respectively, for the station Liman. According to the Kolmogorov-Smirnov test, there is no significant difference between measured and predicted data. Models showed a good performance in forecasting days with the high values over a certain threshold.

The Choice of an Appropriate Information Dissimilarity Measure for Hierarchical Clustering of River Streamflow Time Series, Based on Calculated Lyapunov Exponent and Kolmogorov Measures.

The purpose of this paper was to choose an appropriate information dissimilarity measure for hierarchical clustering of daily streamflow discharge data, from twelve gauging stations on the Brazos River in Texas (USA), for the period 1989-2016. For that purpose, we selected and compared the average-linkage clustering hierarchical algorithm based on the compression-based dissimilarity measure (NCD), permutation distribution dissimilarity measure (PDDM), and Kolmogorov distance (KD). The algorithm was also compared with K-means clustering based on Kolmogorov complexity (KC), the highest value of Kolmogorov complexity spectrum (KCM), and the largest Lyapunov exponent (LLE). Using a dissimilarity matrix based on NCD, PDDM, and KD for daily streamflow, the agglomerative average-linkage hierarchical algorithm was applied. The key findings of this study are that: (i) The KD clustering algorithm is the most suitable among others; (ii) ANOVA analysis shows that there exist highly significant differences between mean values of four clusters, confirming that the choice of the number of clusters was suitably done; and (iii) from the clustering we found that the predictability of streamflow data of the Brazos River given by the Lyapunov time (LT), corrected for randomness by Kolmogorov time (KT) in days, lies in the interval from two to five days.

Analysis of Solar Irradiation Time Series Complexity and Predictability by Combining Kolmogorov Measures and Hamming Distance for La Reunion (France).

Analysis of daily solar irradiation variability and predictability in space and time is important for energy resources planning, development, and management. The natural variability of solar irradiation is being complicated by atmospheric conditions (in particular cloudiness) and orography, which introduce additional complexity into the phenomenological records. To address this question for daily solar irradiation data recorded during the years 2013, 2014 and 2015 at 11 stations measuring solar irradiance on La Reunion French tropical Indian Ocean Island, we use a set of novel quantitative tools: Kolmogorov complexity (KC) with its derivative associated measures and Hamming distance (HAM) and their combination to assess complexity and corresponding predictability. We find that all half-day (from sunrise to sunset) solar irradiation series exhibit high complexity. However, all of them can be classified into three groups strongly influenced by trade winds that circulate in a "flow around" regime: the windward side (trade winds slow down), the leeward side (diurnal thermally-induced circulations dominate) and the coast parallel to trade winds (winds are accelerated due to Venturi effect). We introduce Kolmogorov time (KT) that quantifies the time span beyond which randomness significantly influences predictability.

Spatial and Temporal Non-Linear Dynamics Analysis and Predictability of Solar Radiation Time Series for La Reunion Island (France).

Analysis of daily solar irradiation variability and predictability in space and time is important for energy resources planning, development, and management. The natural intermittency of solar irradiation is mainly triggered by atmospheric turbulent conditions, radiative transfer, optical properties of cloud and aerosol, moisture and atmospheric stability, orographic and thermal forcing, which introduce additional complexity into the phenomenological records. To address this question for daily solar irradiation data recorded during the period 2011-2015, at 32 stations measuring solar irradiance on La Reunion French tropical Indian Ocean Island, we use the tools of non-linear dynamics: the intermittency and chaos analysis, the largest Lyapunov exponent, Sample entropy, the Kolmogorov complexity and its derivatives (Kolmogorov complexity spectrum and its highest value), and spatial weighted Kolmogorov complexity combined with Hamming distance to assess complexity and corresponding predictability. Finally, we have clustered the Kolmogorov time (that quantifies the time span beyond which randomness significantly influences predictability) for daily cumulative solar irradiation for all stations. We show that under the record-breaking 2011-2012 La Nina event and preceding a very strong El-Nino 2015-2016 event, the predictability of daily incident solar energy over La Réunion is affected.