Jeden z ważniejszych problemów w ocenie stanu technicznego ziemnych budowli hydrotechnicznych dotyczy obserwacji procesu filtracji wody i analizy możliwości powstania niekorzystnych zjawisk z nim związanych. Celem przeprowadzonych badań była ocena przydatności spektrometrii impedancyjnej (EIS-Electrical Impedance Spectroscopy) do obserwacji przebiegu procesu filtracji wody przez korpus obwałowania ziemnego. Badania przeprowadzono w korycie hydraulicznym na modelu nasypu obwałowania wykonanego z piasku grubego pylastego. Przepływ filtracyjny wywoływany był poprzez kilkustopniowe podnoszenie i opuszczanie wody w korycie hydraulicznym. Pomiary wartości oporności gruntu odniesiono do rejestrowanego w sposób automatyczny poziomu piezometrycznego wody. Uzyskane wyniki badań wykazały istotną zbieżność zmian oporności gruntu ze zmianami położenia poziomu zwierciadła wody filtracyjnej mierzonymi w piezometrze.

Landslides are well-known phenomena that cause significant changes to the relief of an area’s terrain, often causing damage to technical infrastructure and loss of life. One of the possible means of reducing the negative impact of landslides on people’s lives or property is to recognize areas that are prone to their occurrence. The most common approach to this problem is preparing landslide susceptibility maps. These can factor in the actual location of landslides or the causal relationship between landslides and selected environmental factors. Creating a classification of landslide-prone areas is a challenging task when landslide density is not high and the area of analysis is large. We prepared shallow 10 m × 10 m resolution landslide susceptibility maps of the Wiśnickie Foothills (Western Carpathians, Poland) using eleven different machine learning algorithms derived from the Python libraries Scikit-learn and Imbalanced-Learn. The analyzed area is characterized by a mean density of 3.4 surficial landslides (composed of soils and rocks) per km2. We also compared different approaches to imbalanced sets of data: Logistic Regression, Naive Bayes, Random Forest, AdaBoost, Bagging, ExtraTrees (Extremely Randomized Trees), Easy Ensemble, Balanced Bagging, Balanced Random Forest, RUSBoost and a hybrid model combining Random Under Sampler and Multi-layer Perceptron algorithms. The environmental factors (slope inclination and aspect, distance from rivers, lithology, soil type and permeability, groundwater table depth, profile and plan curvature, mean annual rainfall) were categorized and divided into training (70%) and testing (30%) sets. Accuracy, recall, G-mean and area under receiver operating curve (AUC) were used to validate the quality of the models. The results confirmed that algorithms based on decision tree classifiers are suitable for preparing landslide susceptibility maps. We also found that methods that generate random undersampling subsets (Easy Ensemble, Balanced Bagging, RUSBoost) and ensemble methods (Bagging, AdaBoost, Extra-Trees) both yield very similar test results to those that use full sets of data for training. Relatively high-quality results can also be obtained by integrating the Random Under Sampler algorithm with the Multi-layer Perceptron algorithm.

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Val ues of rain fall thresh olds on se lected shal low land slide slopes (Dzia³, GwoŸdziec) lo cated in the Nowy Wioenicz Foot hills are de ter mined us ing a phys i cally-based slope sta bil ity model con sid er ing a long-term pe riod of anal y sis (GeoSlope Inc. soft ware). Slope sta bil ity anal y sis in cluded de ter mi na tion of the im pact of rain fall on changes of stress state within the soil sub strate and their in flu ence on es ti ma tion of mass move ment risk. The slope sta bil ity cal cu la tion re sults have shown that the rain fall thresh old val ues are a func tion of many vari ables, pri mar ily the hy drau lic prop er ties of soil and rock sub stra tum, tem po ral dis tri bu tion of pre cip i ta tion, and soil mois ture con tent con di tions in the pe riod pro ceed ing rain fall. The re sults of the cal cu la tions in di cate that, in ex treme cases, ac cu mu lated rain fall thresh old val ues for the same slope can range from ~100 to 500 mm. Es ti mated rain fall thresh old val ues were lower than those val ues re ported in the lit er a ture for the Pol ish Carpathians, but are sim i lar to those de ter mined by Guzetti et al. (2007) for Cen tral and South ern Eu rope.