On some new statistical characteristics of the wind “Koshava”

Summary Koshava is a gusty wind of moderate to strong intensity, blowing from the south-eastern direction, over the area of the Republic of Serbia. It is caused by the interaction between the synoptic circulation and the orography of the Carpathian and the Balkan mountains. The Koshava wind can damage buildings, factories and industrial plants or city infrastructure. Therefore it is important to estimate its gust and the gustiness factor on the basis of the measured data.This paper discusses a statistical analysis of wind data in the maximum influence area of the Koshava wind in the periods of maximum duration of Koshava. The focus of the paper is the examination of urban and suburban effects on Koshava wind and the correlation between the instantaneous maximum wind speed and the hourly mean wind speed. The best fitting with various empirical distributions is proposed.With 10 Figures     

Harnessing spatio‐temporal patterns in data for nominal attribute imputation

Missing data in Volunteered Geographic Information (VGI) are an unavoidable consequence of data collection by non‐experts, guided by only vague and informal mapping guidelines. While various Missing Value Imputation (MVI) techniques have been proposed as data cleansing strategies, they have primarily targeted numerical data attributes in non‐spatial databases. There remains a significant gap in methods for imputing nominal attribute values (e.g., Street Name) in map databases. Here, we present an imputation algorithm called the Membership Imputation Algorithm (MIA), targeting spatial databases and enabling imputation of nominal values in spatially referenced records. By targeting membership classes of spatial objects, MIA harnesses spatio‐temporal characteristics of data and proposes efficient heuristics to impute the class name (i.e., a membership). Experimental results show that the proposed algorithm is able to impute the membership with high levels of accuracy (over 94%) when assigning Street Name(s), across highly diverse regional contexts. MIA is effective in challenging spatial contexts such as street intersections. Our research serves as a first step in highlighting the effectiveness of spatio‐temporal measures as a key driver for nominal imputation techniques.     

Diagenesis of diatomite from the Kolubara Coal Basin,Baroševac,Serbia

Diatomite associated with the Kolubara Coal Basin was studied to better understand early stage silica diagenesis of shallow water deposits. The Kolubara Basin consists of Neogene siliciclastic rocks, diatomite, marlstone and rare carbonates. Palaeozoic metamorphic and Mesozoic sedimentary and igneous basement rocks are transgressively overlain by Upper Miocene sandstone, siltstone, shale and mudstone. This Upper Miocene section is transgressively overlain by the Pontian section, which contains diatomite and coal beds. White and grey diatomite forms beds 0.7-2.2 m thick that are continuous over an area of about 2 km². Siliceous rocks vary in composition from diatomite (81-89 per cent SiO₂) to diatom-bearing shale (58-60 per cent SiO₂). Siliceous deposits are laminated in places, with the laminae defined by variations in clay minerals, organic matter and diatoms. Diatomite shows only incipient diagenesis characterized by the fragmentation of diatom frustules, the minor to moderate corrosion of frustules and the formation of minor amounts of opal-A' (X-ray amorphous inorganic opal) cement. The low degree of diagenesis results from the young age of the deposits, low burial temperatures and possibly also from the presence of abundant organic matter and the dissolution of kaolinite. The presence of only weak diagenesis is also reflected by the characteristically poor consolidation of the rocks and low rank of the associated coal.