The data obtained from air quality monitoring stations, which are used to carry out studies using data mining techniques, present the problem of missing values. This paper describes a research work on missing data imputation. Among the most common methods, the method that best imputes values to the available data set is analysed. It uses an algorithm that randomly replaces all known values in a dataset once with imputed values and compares them with the actual known values, forming several subsets. Data from seven stations in the Silesian region (Poland) were analyzed for hourly concentrations of four pollutants: nitrogen dioxide (NO2), nitrogen oxides (NOx), particles of 10 μm or less (PM10) and sulphur dioxide (SO2) for five years. Imputations were performed using linear imputation (LI), predictive mean matching (PMM), random forest (RF), k-nearest neighbours (k-NN) and imputation by Kalman smoothing on structural time series (Kalman) methods and performance evaluations were performed. Once the comparison method was validated, it was determine that, in general, Kalman structural smoothing and the linear imputation methods best fitted the imputed values to the data pattern. It was observed that each imputation method behaves in an analogous way for the different stations The variables with the best results are NO2 and SO2. The UMI method is the worst imputer for missing values in the data sets.
Nowadays the mining companies use the Spatial Information System in order to facilitate data management, gathered during the mining activity. For these purposes various kinds of applications and software information are used. They allow for faster and easier data processing. In the paper there are presented the possibilities of using the ArcGIS system to support the tasks performed in the mining industry in the scope of the analysis of the influence of the mining tremors, induced by the longwall exploitation on the facilities construction sited on the surface area. These possibilities are presented by the example of the database developed for the coal mine KWK “Rydułtowy-Anna.” The developed database was created using ArcGIS software for Desktop 10. 1. It contains the values of parameters, specified for its implementation relevant to the analyses of the influence of the mining tremors on the surface structures.
The problem involving the monitoring of surface ground movements in post-mining areas is particularly important during the period of mine closures. During or after flooding of a mine, mechanical properties of the rock mass may be impaired, and this may trigger subsidence, surface landslides, uplift, sinkholes or seismic activity. It is, therefore, important to examine and select updating methods and plans for long-term monitoring of post-mining areas to mitigate seismic hazards or surface deformation during and after mine closure. The research assumed the implementation of continuous monitoring of surface movements using the Global Navigation Satellite System (GNSS) in the area of a closed hard coal mine ‘Kazimierz-Juliusz’, located in Poland. In order to ensure displacement measurement results with the accuracy of several millimetres, the accuracy of multi-GNSS observations carried out in real time as a combination of four global navigation systems, Global Positioning System (GPS), Globalnaja Navigacionnaja Sputnikova Sistema (GLONASS), Galileo and BeiDou, was determined. The article presents the results of empirical research conducted at four reference points. The test observations were made in variants comprising measurements based on: GPS, GPS and GLONASS systems, GPS, GLONASS and Galileo systems, GPS, GLONASS, Galileo and BeiDou systems. For each adopted solution, daily measurement sessions were performed using the RTK technique. The test results were subjected to accuracy analyses. Based on the obtained results, it was found that GNSS measurements should be carried out with the use of three navigation systems (GPS, GLONASS, Galileo), as an optimal solution for the needs of continuous geodetic monitoring in the area of the study.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.