No abstract

The Fernpass rockslide occurred about 4100 years ago and is one of the largest rock slope failures in the Alps, the run-out area being characterized by a hummocky surface with Toma Hills that were formed after the rockslide event. These hills, characterized by a cone-like shape with a flattened top, are typical for many rockslides, and the name BToma^is derived from local dialects in Switzerland and Tyrol, Austria. So far, five hypotheses have been proposed for the formation of Toma Hills, the two relating to glacial activity being outdated meanwhile. These hypotheses cannot explain all features observed on site. Therefore, to investigate one of these hypotheses, a qualitative laboratory-scale analogue groundwater flow model with the size 1.5 × 1 × 0.8 m was used to investigate the contribution of internal erosion by suffosion to forming Toma Hills. From the model, hydrogeological calculations of Darcy velocity and hydraulic conductivity were determined to help understand the present study. The average hydraulic gradient of the experiments was close to that in reality. Hydraulic conductivities in the five experiments were comparable to the calculated field hydraulic conductivity (Bialas and Seelheim equations). A limnokrene like one southwest of Biberwier developed in all experiments. Development of lateral and transversal cracks, water-filled lateral depressions of the material in the model and discharge of finer material implicated that internal erosion is a substantial contribution to the Toma Hill formation. Based on the experimental results, we deduce that Toma Hill formation took between 140 and 580 years.

No abstract

No abstract

Collecting mining influenced water (MIW) quality data can result in incomplete data sets with missing values and anomalies, making it challenging to use the data for optimizing mine water management. This work explores advanced statistical data analysis approaches for addressing missing data interpolation and anomaly detection in MIW data sets. The study compares the performance of five different interpolation techniques and four different anomaly detection techniques using supervised and unsupervised machine learning algorithms developed using Python 3.8.16. The results of the study demonstrate that the radial basis function, spline, and k-nearest-neighbors interpolation techniques, along with the predictive confidence interval level anomaly approach based on gradient boosting regression trees, perform best for missing data interpolation and anomaly detection, respectively. Thorough application of these advanced techniques can improve the accuracy and reliability of mine water quality data, which is crucial for making conclusions on the safety of the environment, public health, and effective MIW management. This paper highlights the importance of developing effective methods for addressing missing data and anomalies in MIW data sets, which can ultimately lead to improved treatment plant optimization.