## 1 Introduction  
For the process of production of mineral resources in underground mines to proceed smoothly, it is necessary to ensure the functionality and stability of the underground rooms. Sometimes, to ensure both functionality and stability of the underground roadways, which are directly related, it is necessary to install a suitable support substructure. The support takes static and dynamic loads from the rock mass and begins to deform over time. For the unhindered passage of loading and transport machinery, it is necessary to design the underground roadway considering the minimum dimensions of the free cross-section of the underground roadway, including the legally defined minimum safety distances from the equipment to the walls and roof of the roadway. As time passes, the underground support suffers deformations, and it can happen that the minimum dimensions are violated, after which the transportation and export of useful mineral resources are suspended. In such cases, it is necessary to proceed with the reconstruction of the underground roadway, which can have a significant impact on the economic operation of the mine, because the reconstruction is both a financially and time-consuming process that impairs continuous production. To avoid such situations, it is necessary to conduct systematic monitoring of deformations of the support construction and provide a forecast of deformations. With proper forecasting, mining engineers can react in a timely manner and plan the replacement of the underground support in the necessary sections of the underground roadway. In this way, strategic management of the functionality of underground rooms and continuous production is achieved. The use of a laser scanner or similar instrument with the possibility of gathering high-accuracy data is necessary to obtain deformation data that can be used for analysis and forecasting the future states of the support construction. Various approaches can be used to obtain future states of support construction. Deformation forecast has been the subject of research by many authors, with different approaches for prediction of tunnel surrounding rock displacement, structural deformations prediction, deformations of the lake bottom, landslide deformations, slope deformations, displacements of mining roadways, and so on. [PERSON] et al. [1] used two methodologies, support vector machines and artificial neural networks, to predict tunnels surrounding rock displacements; though the support vector machine gave more accurate predictions, it was more time-consuming than the artificial neural network. [PERSON] et al. [2] proposed a model based on temporal convolutional networks in their study for structural deformation prediction, which they verified with the cumulative strain data of the upper steel beam in the foundation pit in China as well as the structural subsidence data on the same location. [PERSON] et al. [3] showed that the grey model GM(1,1) could be used for the prediction of the deformations on the lake bottom. [PERSON] et al. [4] used tunnel geological information as well as monitoring measurement data to determine factor weights, and extension theory was used for the prediction model of tunnel deformations. In their study, [PERSON] et al. [5] used the grey model theory for the prediction of the large deformation of the tunnel. [PERSON] et al. [6] established a grey forecast model for rock deformation in a big tunnel cross section and concluded that it could be used in engineering practice. Prediction of the final displacement of underground structures based on the improved no equidistant grey Verhulst model was used by [PERSON] et al. [7]. [PERSON] [8] predicted displacements in tunnels surrounding rock using the grey system theory. For landslide deformations, displacements were predicted by a model combining extreme learning machines and grey wolf optimization by [PERSON] et al. [9], and by using a new grey model prediction by [PERSON] et al. [10] as well as [PERSON] and [PERSON] [11].