###### Abstract  
Monitoring the stability of tailings storage facilities (TSFs) is extremely important due to the catastrophic consequences of instability, which pose a threat to both the environment and human life. For this reason, numerous laboratory and field tests are carried out around dams. An extensive database is collected as part of monitoring and field research. The in-depth analysis of available data can help monitor stability and predict disaster hazards. One of the important factors is displacement, including surface displacements--recorded by benchmarks as well as underground displacements--recorded by inclinometers. In this work, methods were developed to help assess the stability of the TSF in terms of surface and underground displacement based on the simulated data from geodetic benchmarks. The context of spatial correlation was investigated using hot spot analysis, which shows areas of greater risk, indicating the places of correlation of large and small displacements. The analysis of displacements versus time allowed us to indicate the growing exponential trend, thanks to which it is possible to forecast the trend of future displacements, as well as their velocity and acceleration, with the coefficient of determination of the trend matching reaching even 0.97. Additionally, the use of a geographically weighted regression model was proposed to predict the risk of shear relative to surface displacements. p 2024 14 10715 1  
For these reasons, constant monitoring of dam stability is crucial for early detection and prevention of potential threats. Understanding the sources of failure and implementing prevention methods is essential for ensuring safety. The main causes of TSF failures are slope instability, earthquake loading, overtopping, unsuitable foundations, and seepage [1]. Seismic activity is strongly dependent on the region and in some of them is not relevant, while overtopping can be controlled by proper dam construction and mine production planning. Thus, in this work we focus primarily on slope instability. TSF structures are immensely intricate geotechnical entities, and assuring their stability mandates exhaustive testing, both within the field and in laboratory settings. These tests yield voluminous datasets, encompassing diverse spatial data types, necessitating the formulation of uniform analysis methods. Consequently, this will aid in discerning decision patterns to assist experts. The literature houses a plethora of dam stability analysis methods, which consider a spectrum of security-related concerns and harness data from a range of test types. For instance, in [2], capillary water and its impact on dam stability were examined. The authors demonstrated that capillary phenomena (caused, among other things, by violent rainstorms) have a significant impact on dams, even for several decades after the closure of the TSF. In articles [3; 4], authors discussed monitoring systems for dam stability, emphasizing the use of the Internet of Things and cloud computing technologies. Simulation is also an important aspect, as it helps to predict potential scenarios. The authors of article [5] addressed this problem and proposed a simulation method for determining the stability of a closed TSF for solid waste storage. In article [6], the authors proposed an innovative approach to the seepage and stability of tailings dams using numerical simulation. They developed a finite element model based on engineering data from the Livi tailings dam and presented methodologies for calculating seepage and stability, as well as the total head chart, phreatic line, and safety coefficient of seepage under different working conditions. In turn, ref. [7] demonstrates the efficacy of the finite element method in simulating the consolidation behavior of an upstream tailings dam and reducing the required volume of rockfill Derm to ensure dam stability during the phased construction process. Research [8] emphasizes the important problem of instability that can occur in tailing dams made solely from waste rocks.