The dams are very important objects (production of electric energy, flood management, drought control, etc.) but they are also a great danger for areas downstream because there is always risk of dam failure. To prevent dam failure it is important to perform regular dam monitoring. Precise geodetic surveying of a number of discrete points, which accurately depict the characteristics of the dam, is a common monitoring method. It is not uncommon that some discrete points can't be monitored because of different obstacles or equipment limitations but it is possible to interpolate those dam displacements using prediction. In this research a non conventional approach for interpolation of missing dam displacements data using artificial neural networks (ANNs) is presented. This approach combines spatial and temporal aspects of data to its benefits to give good results using ANNs. Three ANN types (Feed Forward Back Propagation, Cascade Forward Back Propagation and Layer Recurrent Back Propagation) were used for dam displacements prediction. Additionally, a voting system with four functions (“minimum”, “maximum”, “mean of the closest two” and “mean of three”) is introduced to improve prediction results. The research showed that ANNs in combination with the voting system can provide precise prediction for missing dam displacements.

Hydropower dam displacement is influenced by various factors (dam ageing, reservoir water level, air, water, and concrete temperature), which cause complex nonlinear behaviour that is difficult to predict. Object deformation monitoring is a task of geodetic and civil engineers who use different instruments and methods for measurements. Only geodetic methods have been used for the object movement analysis in this research. Although the whole object is affected by the influencing factors, different parts of the object react differently. Hence, one model cannot describe behaviour of every part of the object precisely. In this research, a localised approach is presented—two individual models are developed for every point strategically placed on the object: one model for the analysis and prediction in the direction of the X axis and the other for the Y axis. Additionally, the prediction of horizontal dam movement is not performed directly from measured values of influencing factors, but from predicted values obtained by machine learning and statistical methods. The results of this research show that it is possible to perform accurate short-term time series dam movement prediction by using machine learning and statistical methods and that the only limiting factor for improving prediction length is accurate weather forecast.

Single-beam echo sounders have gained popularity for various applications due to their compact dimensions, ease of use, and cost-effectiveness. The question that often arises among the users is whether these devices can fulfill the necessary accuracy requirements. This paper concentrates on assessing the accuracy that can be achieved using a single-beam echo sounder. An accuracy assessment was performed by comparing the depths derived from the 3D model created from the single-beam echo sounder data to those obtained through more accurate and independent method (tacheometric surveying) in the test area. Accurate depth determination was achieved through trigonometric leveling, employing a specific methodology that allows for precise depth measurements up to 4.5 meters. The assessment results were compared to the vertical accuracy requirements for surveying and mapping in shallow waters, recommended by the International Hydrographic Organization. The results indicate that, with a 95% probability, the depths determined by the single-beam echo sounder meet the total vertical uncertainty (TVU) requirements specified by the S-44 standard for Order 1a survey.

By using specialized geodetic networks, the best available instruments and methods, geodetic engineers try to assess stability of infrastructure objects. When stability of tall, slender objects is inspected, the main stability criterion is the object’s inclination, and some common geodetic approaches in structural health monitoring (SHM) cannot be used in this case. The challenge is greater if the objects do not have clearly defined edges but have cylindrical shape, which is common in architecture (industrial chimneys, silos, water towers, and wind turbine pillars). In this research, an algebraic circle fitting, two-dimensional (2D) linear regression, and bootstrapping model were used to assess inclination of an object from point cloud observations obtained from a terrestrial laser scanner (TLS). It is not always possible to observe the whole object of interest, so in this research, various cases regarding distribution of observations on the object’s surface were analyzed. The testing data sets were simulated by using MATLAB software, but also real-world data obtained by geodetic TLS was used to test the proposed model. The results showed that the proposed approach can give quality estimation of the object’s inclination for cases in which only a part of the object is observed.