Vol. 40, No. 1 (2023)

1.  Sládek, D. (2023): Application of the Random Forest method on the observation dataset for visibility nowcasting.

Accurate visibility forecasting is essential for safe aircraft operations. This study examines how various configurations of the Random Forest model can enhance visibility predictions. Preprocessing techniques are employed, including correlation analysis to identify fundamental relationships in eather observations. Time-series data is transformed into a regular Data Frame to facilitate analysis. This study proposes a classification framework for organizing visibility data and phenomena, which is then used to develop a visibility forecast using the Random Forest method. The study also presents procedures for hyperparameter tuning, feature selection, data balancing, and accuracy evaluation for this dataset. The main outcomes are the Random Forest model parameters for a three-hour visibility forecast, along with an analysis of errors in low visibility forecasts. Additionally, models for one-hour forecasts and visibility forecasting under precipitation are also examined. The resulting models demonstrate a deterministic forecast accuracy of approximately 78%, with a false alarm rate of around 6%, providing a comprehensive overview of the capabilities of the Random Forest model for visibility forecasting. As anticipated, the model demonstrated limitations in accurately simulating fast radiative cooling or abrupt decreases in visibility caused by precipitation. Specifically, in relation to precipitation, the model achieved an accuracy of 79%, yet exhibited a false alarm rate of 19%. Additionally, this method sets a foundation for enhancing prediction accuracy through the inclusion of supplementary forecast data, while its implementation on real-world datasets expands the reach of machine learning techniques to the members of the meteorological community.

Keywords: eaviation meteorology, visibility forecasting, nowcasting, landing forecast (trend), machine learning, random forest, feature selection, hyperparameters tuning

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2.  Kuki, A., S. Lipcsei, I. Gere, F. Járai-Szabó, A. Gergely, D. Ugi, P. D. Ispánovity, Z. Dankházi, I. Groma and Z. Néda (2023): Statistical analogies between earthquakes, micro-quakes in metals and avalanches in the 1D Burridge-Knopoff model.

Universalities and intriguing analogies in the statistics of avalanches are revealed for three physical systems defined on largely different length and energy scales. Earthquakes induced by tectonic scale dynamics, micro-scale level quakes observed from slipping crystallographic planes in metals and a one-dimensional, room-scale spring-block type Burridge-Knopoff model is studied from similar statistical viewpoints. The validity of the Gutenberg-Richter law for the probability density of the energies dissipated in the avalanches is proven for all three systems. By analysing data for three different seismic zones and performing acoustic detection for different Zn samples under deformation, universality for the involved scaling exponent is revealed. With proper parameter choices the 1D Burridge-Knopoff model is able to reproduce the same scaling law. The recurrence times of earthquakes and micro-quakes with magnitudes above a given threshold present again similar distributions and striking quantitative similarities. However, the 1D Burridge-Knopoff model cannot account for the correlations observed in such statistics.

Keywords: earthquakes, micro-plasticity, avalanches, universalities, scaling, correlations, Burridge-Knopoff model

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3.  Gümüş, V., Y. Avşaroğlu, O. Şimşek and L. D. Dinsever (2023): Evaluation of meteorological time series trends in Southeastern Anatolia, Turkey.

In this study, trend analyses of six climatic variables (mean, minimum, and maximum temperature, relative humidity, wind speed, and precipitation) for 1966-2020 are conducted for the Southeastern Anatolia Region, which is the main focus of the integrated development project in Turkey (Turkish acronym GAP). The trends for seasonal and annual periods are determined using the Mann-Kendall (MK) test, and Sen's slope method and regression analyses are used to find the trends' slopes. Moreover, Innovative Trend Analysis (ITA) is also used to find the time series changes for low, medium, and high values. As a result of the analyses, the mean, minimum, and maximum temperatures in the GAP region show increasing trends according to both methods. Significant trends are obtained at a limited number of stations for the precipitation, relative humidity, and wind speed with the MK test, while consistent decreasing trends are found at most stations with the ITA method.

Keywords: trend analysis, climatic variables, Mann-Kendall test, innovative trend analysis, Sen's slope

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4.  Javed, A., Z. Shao, B. Bai, Z. Yu, J. Wang, I. Ara, M. E. Huq, M. Y. Ali, N. Saleem, M. N. Ahmad, N. S. Sumari and Mardia (2023): Development of normalized soil area index for urban studies using remote sensing data.

This paper presents two novel spectral soil area indices to identify bare soil area and distinguish it more accurately from the urban impervious surface area (ISA). This study designs these indices based on medium spatial resolution remote sensing data from Landsat 8 OLI dataset. Extracting bare soil or urban ISA is more challenging than extracting water bodies or vegetation in multispectral Remote Sensing (RS). Bare soil and the urban ISA area often were mixed because of their spectral similarity in multispectral sensors. This study proposes Normalized Soil Area Index 1 (NSAI1) and Normalized Soil Area Index 2 (NSAI2) using typical multispectral bands. Experiments show that these two indices have an overall accuracy of around 90%. The spectral similarity index (SDI) shows these two indices have higher separability between soil area and ISA than previous indices. The result shows that percentile thresholds can effectively classify bare soil areas from the background. The combined use of both indices measured the soil area of the study area over 71 km2. Most importantly, proposed soil indices can refine urban ISA measurement accuracy in spatiotemporal studies.

Keywords: soil index, NSAI1, NSAI2, LULCC, Dhaka

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5.  Gladoviĉ, D., J.Parlov, D. Perkoviĉ, Z. Nakiĉ and Z. Kovaè (2023): The role of slope inclination, doline density and water budget analysis in delineation of complex karst catchment area of Slunjèica River (Croatia).

Due to the high vulnerability of the karst aquifer to the surface contaminants, a precisely defined catchment area has the highest priority. In this study, the influence of slope inclination, the doline density analysis, and the water budget analysis in the delineation process of a complex karst catchment area is discussed. To define hydrogeological role of lithological units, cross sections of slope inclination and doline density were combined with hydrogeological cross sections, while the degree of karstification was used to describe the permeability of rock units. The verification of karst catchment delineation area was performed with water budget analysis. The methodology used for the determination of hydrogeological behavior and delineation of a complex karst catchment area (Slunjèica River basin, Croatia) is shown with the flow diagram. It has been found that the highest doline density appears in the range from 0 to 1° of the slope inclinations, and that it decreases with a higher slope degree. Although the results of this study confirm that even with the relatively small number of input data it is possible to define the karst catchment area, it must be emphasized that the doline density analysis presents an indispensable tool in the research related to the definition of karst catchment areas.

Keywords: karst catchment, doline density, slope inclination, water budget analysis, Slunjèica River (Croatia)

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