Recently, there is digital transformation of university education, including new concepts for obtaining and presenting learning materials. In article, there is considered digital learning technologies for geo-information management within Industry 4.0 while modern global economic crisis in last years. In research, there are used Foresight technologies, theory of decision making under uncertainties and risk management. Also, there are used methods of data bases constructing, web-technologies and virtual reality tools. As base technology, authors propose to use digital educational platforms, which integrate heterogeneous hardware and software resources using web technologies in distributed networks and a wide use of cloud services. Authors propose to use Google Classroom as essential digital educational platform. As study result, there are proposed enlarged groups of didactic works in geo-information management, oriented on practical purposes and adapted to Covid-19 pandemic conditions. There are considered the issues of digital content creation within university education, essentially in practical training. Presented in article results of study have a significant scientific novelty and can be used in educational and training purposes, including the preparation of Master's programs in geo-information management.
The problem of assessing out-of-sample forecasting performance of event-history models is considered. Time-to-event data are usually incomplete because the event of interest can happen outside the period of observation or not happen at all. In this case, only the shortest possible time is observed and the data are right censored. Traditional accuracy measures like mean absolute or mean squared error cannot be applied directly to censored data, because forecasting errors also remain unobserved. Instead of mean error measures, researchers use rank correlation coefficients: concordance indices by Harrell and Uno and Somers’ Delta. These measures characterize not the distance between the actual and predicted values but the agreement between orderings of predicted and observed times-to-event. Hence, they take almost “ideal” values even in presence of substantial forecasting bias. Another drawback of using correlation measures when selecting a forecasting model is undesirable reduction of a forecast to a point estimate of predicted value. It is rarely possible to predict the timing of an event precisely, and it is reasonable to consider the forecast not as a point estimate but as an estimate of the whole distribution of the variable of interest. The article proposes computing Cox–Snell residuals for the test or validation dataset as a complement to rank correlation coefficients in model selection. Cox–Snell residuals for the correctly specified model are known to have unit exponential distribution, and that allows comparison of the observed out-of-sample performance of a forecasting model to the ideal case. The comparison can be done by plotting the estimate of integrated hazard function of residuals or by calculating the Kolmogorov distance between the observed and the ideal distribution of residuals. The proposed approach is illustrated with an example of selecting a forecasting model for the timing of mortgage termination.
Subject. This article analyzes the risks of natural and man-made origin induced in environmental management in the coastal system of the Arctic zone of the Russian Federation in the face of global climate change and ever-increasing anthropogenic impacts.
Objectives. The article aims to classify the risks of Arctic coastal nature management, and determine their sources and factors of origin.
Results. The article presents a phased system of risk structuring as a process of causing harm that is likely to be implemented. It identifies classification criteria and specific forms of structural elements of risk, and conducts an expert assessment of these relationships.
Relevance. The results obtained help identify key elements of various scenarios of risk occurrence in the Russian Arctic coastal nature management, including the risk of cascading disasters.
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