Fall 2022
Marc O’Callaghan: Comparison of YOLOv4-tiny and YOLOv7-tiny for instream wood detection
Julia Walker: Comparing Machine Learning Algorithms for Land Cover Classification for Vallon de Nant, Switzerland
Fabien Augsburger: Machine Learning Prediction of Moderate and Extreme Wind Bursts in Europe
Étienne Delaloye: Using a Random Forest Classifier and Logistic Regression for Binary Classification of Water Potability
Axelle Bersier: Exploratory analysis of clustering Swiss population using Self-Organizing Maps
Margaux Hofmann: Predicting Snow Line Elevation and Glacier Activity using Machine Learning
Elias Al Alam: Detection of Oil Spills Using Machine Learning
Doruntina Bekolli: Prediction of Rainfall in Australia Using Logistic Regression and Random Forest Algorithms
Douglas Stumpp: Extracting road networks from satellite images with neural networks to contribute to risk mitigation
Haakon Vikesaa: Comparing Logistic Regression and Random Forest Classifiers for Landslide Hazard Assessment in Vaud, Switzerland
Melinda Femminis: Quantitative estimation of the area affected by the 2022 Jagersfontain tailings dam’s collapse
Emmanuel Emezina: Assessment of the EuroSAT Dataset by applying Deep Learning Techniques
Florent Rouge: Instream wood detection using the YOLOv4 algorithm on aerial images of the Spöl river
Max Henking: Classifying train delays based on environmental variables
Jérémie Fragnière: Clustering Tropical Cyclogenesis Events Based on Environmental Predictors and Oceanic Basins
Christophe Reymond: Instream wood detection using the YOLOv4 object detection algorithm
Faye Perchanok: Predicting proglacial lake discharge using machine learning algorithms
Jonathan Cotasson: Comparing algorithms for storm cluster detection
Janbiro Ntamushobora: Tracking the evolution of gentrification in the neighborhoods of Paris
Baptiste Poffet: Quantifying deforestation in the Amazon forest using machine learning
Thomas Krieger: Predicting extreme events with Random Forest and K-Nearest Neighbors
Spring 2022
Isabel Nicholson Thomas: Can machine learning be used to generate accurate and regular Land Cover data for Switzerland from satellite imagery?
Christophe Reis: Predicting the exposure concentration of a pollutant on tadpoles based on their behavior and physical traits
Alessio Poloni: Ranking Features for Landslide Prediction
Tabea Cache: Coupling stochastic rainfall images and data-driven flood emulation for fast urban flood mapping
Yasmin Ghadyani: Deriving Lake Urmia water depth using Landsat 8 imagery and field measurements
Joël Davila Estevez: Estimating the Hazardousness of Urban Waters from the Pollutant Concentrations
Luca Eiholzer: Comparing performance of Random Forest and SVM for shallow landslides identification
Gabriel Juri A.: Benchmarking imputation methods on the implementation of GRU to forecast Mendota lake’s epilimnetic phosphorus
Jeremy Keller: Zooplankton images classification using machine learning
Josephine S. Kramer: Trace and evaluation of the dependencies of Radon222 on air pressure and temperature using machine learning tools
Renaud Nasch: Micropollutants assessment of Vidy Bay backed by machine learning algorithms
Aldo Fornari: Instream LargeWood detection trough YOLOV4
Vincenzo Guzzardi: Prediction of Ammonium concentration in a river using unsupervised Machine Learning
Alessandro Giovanardi: Suspended bedload in a proglacial river
Keyvan Diba: Using machine learning regarding suspended sediment concentration in a proglacial river