## 2 Current Situation  
Groundwater level measurements have a long history, but one of the first cities which can boast off a complex analysis of its hydrogeological conditions is the city of Wroclaw (Breslau). In 1876, the groundwater level in the city has been characterized for the first time by [PERSON] [6]. He introduced general statistics of the groundwater level based on irregular measurements in few boreholes. Later, a network of 43 observation wells has been established by the City Council and regular measurements have been running by firefighters until 1922. All the results have been published in the statistical yearbooks 'Breslauer Statistik'. After this period only irregular and short-term observations in the city were conducted [7]. The importance of groundwater level observations has been emphasized by several studies in Germany and Austria since the end of the 19 th century, but a more recent one in English language after [PERSON]'s analysis is from [PERSON] and [PERSON] [5]. They have explained basic rules of groundwater level monitoring and present case studies to highlight the wide range of applicability of long-term groundwater level data to varied water issues related to geoengineering or water management. Groundwater monitoring data analysis has for decades been based on simple statistical methods [8]. Groundwater level prognosis had been primarily developed based on analytical methods and physical models [9]. In the 1990 ies, numerical modeling with the implementation of the finite element method (FEM) or finite difference method (FDM) became common in predictions of groundwater conditions [10] as well as in predicting groundwater level and spatio-temporal analyses of climatic factors [11; 12; 13]. The last 15 years have also brought rapid development of other methods in research on Earth's water system trends and on a description of groundwater level fluctuations phenomena. Implementation of wavelet analysis to evaluate factors controlling groundwater level fluctuations in a riverside alluvial aquifer was done by [PERSON] et al. [14]. Combination of wavelets and neural networks has been used to forecast groundwater levels [15]. Groundwater level time series analysis and its relationships to different factors such as precipitation had also been studied using fractals [16; 17; 18]. There are also many works based on the implementation of different types of neural networks to groundwater analysis [19], as well as autoregressive models  
Figure 1: Cross-section of a typical groundwater flow system (modified from: [5]). and advanced statistical methods for groundwater levels characteristics and predictions [20; 21; 22; 23] including robust detrended analysis [24]. Groundwater potential mapping in the Dak Lak Province, Vietnam, implementing soft computing methods combined with advanced algorithms has been done by [PERSON] et al. [25]. We can easily find the interconnection of cited research with methods applied to the analysis of other environmental processes [26]. One may notice universal problems as validation [27], segmentation [28; 29], regime change [28; 30], denoising [31], modelling [32] or processes separation [33] with using pattern extraction [28; 33; 34]. Often such analysis is performed in a multidimensional data space [30], where the problem of transformation to a specific domain or simple selection of informative features in multidimensional data space is needed [35]. There are some papers presenting the mentioned methods that are used for the analysis of groundwater issues [36; 37; 38; 39; 40; 41], but they are not specifically focusing on groundwater level analysis but on the hydrogeochemistry or clustering the observation points. One of those studies [42] used Singular Value Decomposition (SVD) and a Cross-Wavelet Approach (CWA) to analyze the spatiotemporal groundwater level--precipitation relationship of 50 wells. The authors identified four time response lags and pointed out the importance of complete time series, especially of the precipitation near the wells. [PERSON] et al.