A log data analysis plays an important role in the uranium mining process. Automating this analysis using machine learning methods improves the results and reduces the influence of the human factor. In particular, the identification of reservoir oxidation zones (ROZs) using machine learning allows a more accurate determination of ore reserves, and correct lithological classification allows the optimization of the mining process. However, training and tuning machine learning models requires labeled datasets, which are hardly available for uranium deposits. In addition, in problems of interpreting logging data using machine learning, data preprocessing is of great importance, in other words, a transformation of the original dataset that allows improving the classification or prediction result. This paper describes a uranium well log (UWL) dataset generated with the employment of floating data windows and designed to solve the problems of identifying ROZ and lithological classification (LC) on sandstone-type uranium deposits. Comparative results of the ways of solving these problems using classical machine learning methods and ensembles of machine learning algorithms are presented. It has been shown that an increase in the size of the floating data window can improve the quality of ROZ classification by 7–9% and LC by 6–12%. As a result, the best-quality indicators for solving these problems were obtained, f1_score_macro = 0.744 (ROZ) and accuracy = 0.694 (LC), using the light gradient boosting machine and extreme gradient boosting, respectively.

Urban air pollution poses a serious threat to human health. To monitor it, both individual sensors and systems are used to assess the concentration of dust particles PM1, PM2.5, PM10 and organic compounds. However, the reliability of the sensor system cannot be 100 percent. From time to time, certain sensors in a distributed system fail. For this reason, it is very useful to emulate their readings based on the readings of the remaining sensors. The work describes a data set and proposes a machine learning model, which, based on the readings of functional sensors and weather conditions at the data collection sites, simulates the readings of a failed sensor. The accuracy of such emulation for certain types of pollution has been assessed (the coefficient of determination ranges from 0.43 to 0.61).