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Geological and palaeogeographical peculiarities of the Adamów Graben area, central Poland

Widera Marek, Dzieduszyńska Danuta, Petera-Zganiacz Joanna

Geologos

2022

Vol. 28, No. 1

1--17

From a geological and palaeogeographical point of view, the area of the Adamów Graben in the vicinity of Turek ranks amongst the best known in central Poland, with several opencast mines located here where lignite was exploited for 57 years. These large-surface exposures provide a good opportunity for detailed geological studies of strata of Late Cretaceous to Holocene age. However, the present research focuses mainly on those deposits, forms and structures that have been most thoroughly examined and are best exposed. These are Cretaceous marls and gaizes, Paleogene ‘blue clays’ and the ‘Koźmin Gravels’, Neogene sandstones, as well as the Quaternary glacial ‘Lake Koźmin’, involutions and ‘Koźmin Las’. Some of these, e.g., the ‘Koźmin Gravels’ and ‘Koźmin Las’, are not known from other Polish territories. Furthermore, results obtained by the authors over a period of nearly 30 years also include data on palaeogeographical changes across some Cenozoic intervals, especially during the early Oligocene and late Weichselian.

Forecasting surface water-level fluctuations of a small glacial lake in Poland using a wavelet-based artificial intelligence method

Piasecki A., Jurasz J., Adamowski J. F.

Acta Geophysica

2018

Vol. 66, no. 5

1093--1107

Lake waters are a significant source of drinking water and contribute to the local economy (e.g. enabling irrigation, offering opportunities for tourism, waterways for transport, and meeting utility water demands); therefore, the ability to accurately forecast lake water levels is important. However, given the significant lack of research with respect to forecasting water levels in small lakes (i.e. 0.05 km2\area\10 km2), the present study sought to address this knowledge gap by testing a pair of hypotheses: (1) it is possible to forecast water levels in small surface lakes using artificial neural networks (ANN), and (2) better water-level forecasts will be obtained when the wavelet transform (WT) is used as an input data preprocessing tool. Based on an analysis of a case study in Lake Biskupinskie (1.16 km2) in Poland and based on a range of model performance statistics (e.g. mean absolute error, root mean square error, mean squared error, coefficient of determination, mean absolute percentage error), both hypotheses were confirmed for monthly forecasting of lake water levels. ANNs provided good forecasting results, and WT pre-processing of input data led to even better forecasts. Additionally, it was found that meteorological variables did not have a significant impact in forecasting water-level fluctuations. In light of the results and the limited scope of the present study, proposed future research directions and problems to be resolved are discussed.