Im Beitrag werden zwei alternative Methoden zur inversen Schätzung der Parameter für Bodenbewegungsvorausberechnungsmodelle aus Messdaten zu Höhenänderungen an Höhenfestpunkten gegenübergestellt, ein Ansatz unter Nutzung der Ausgleichungsrechnung sowie ein Bayes’scher Ansatz unter Nutzung der Monte-Carlo-Simulation. Der Vergleich erfolgt im Kontext eines Kavernenfeldes. Es wird gezeigt, dass durch beide Verfahren aus Höhenbeobachtungen an der Tagesoberfläche die Parameter Hohlraumkonvergenz und Einwirkungswinkel signifikant präzisiert werden können, was zu verbesserten Vorhersagen führt. Im Ergebnis der Studie lassen sich Möglichkeiten ableiten, das Messnetz zu optimieren. / The paper compares two alternative methods for inverse estimation of the parameters for ground movement prediction models from elevation change measurements at fixed levelling points, an approach using the geodetic adjustment theory and a Bayesian approach using Monte-Carlo simulation. The comparison is performed in the setting of a cavern field. It is shown that both methods allow utilizing elevation-change observations on the surface to significantly improve the prediction of the parameters convergence and angle of influence. Such an approach will lead to improved predictions. As a result of the study, opportunities for optimizing the elevation measurement network can be lifted.

This work deals with the and preemptive road users behaviour safety analysis problem. Safety analysis is based on a processing of road users trajectories obtained from processed aerial videos captured by drons. A system for traffic conflicts detection from spatial-temporal data is presented in this work. The standard approach for pro-active traffic conflict indicators evaluation was extended by simulating traffic objects movement in the scene using Ackerman steering geometry in order to get more accurate results.

Stock market analysis is a hot-button topic, especially with the growth of online communities surrounding trading and investment. The goal of this paper is to examine the sentiment of r/wallstreetbets and the Google Trends score for a number of stocks – and then understanding whether the herding nature of investors on r/wallstreetbets is better at predicting the movement of the stock market than the WOC nature of Google Trends scores. Some combination of the herding and WOC values will also be used in predicting stock market fluctuations. Analysis will be done through the machine learning algorithms RFC and MLP. Through the mean and median precisions presented by the different machine learning algorithms the effectiveness of the predictor can be understood. This paper finds no real connection between either r/wallstreetbets sentiment or Google Trends data regarding predicting stock value fluctuations – with r/wallstreetbets yielding approximately 51%-52% mean precision depending on the machine learning algorithm used, and Google Trends precisions sitting at around 51%. The combination of r/wallstreetbets data and Google Trends data did not produce any significantly higher precision either, being between 51%-52%.