{"id":980,"date":"2022-08-28T16:13:35","date_gmt":"2022-08-28T14:13:35","guid":{"rendered":"https:\/\/wp.unil.ch\/dawn\/?page_id=980"},"modified":"2025-10-08T13:30:02","modified_gmt":"2025-10-08T11:30:02","slug":"ml_ees_projects","status":"publish","type":"page","link":"https:\/\/wp.unil.ch\/dawn\/ml_ees_projects\/","title":{"rendered":"Final Projects \u2013 ML for Earth & Env. Sciences"},"content":{"rendered":"\n

<\/p>\n\n\n\n

Fall 2024<\/summary>\n

Justine Berg: Combining a Machine Learning model with a global glacier model for predictions of water resources in high mountain catchments<\/a><\/p>\n\n\n\n

Baptiste Biscal: Automatic Identification of Fish Profile Based on Deep Learning<\/a><\/p>\n\n\n\n

Mathilde Dunand: Comparing performance of different Machine Learning algorithm for shallow landslides prediction<\/a><\/p>\n\n\n\n

Joseph Grob: Predicting County-Level Diabetes Prevalence in the United States Using Machine Learning and Geospatial Analysis<\/a><\/p>\n\n\n\n

Ayoub Hartaoui: Classifying Amphiprion Ocellaris Using a Custom CNN Architecture: A Comparative Study with a pre-build CNN for Ecological Monitoring<\/a><\/p>\n\n\n\n

Cecilia Torres: Comparing Machine Learning Algorithms for Predicting Biome Type from Soil Microbial Communities<\/a><\/p>\n\n\n\n

Louise Estebe: Comparing various machine learning algorithms for multiclass Classification of Plant Features in User-Submitted Images<\/a><\/p>\n\n\n\n

Jonas Woodtli: Filling temporal gaps in chlorophyll-a in-situ measurements using machine learning<\/a><\/p>\n\n\n\n

Charlotte Grosjean: Anomaly detection for precipitation time-series dataset<\/a><\/p>\n\n\n\n

Lara Sauser: Filling gaps in NDVI images using Convolutional Neural Network<\/a><\/p>\n\n\n\n

Samuel Lutz: Predicting the future water surface temperatures of Lake Geneva using classic Machine Learning algorithms<\/a><\/p>\n\n\n\n

Th\u00e9ophile Zuber: Influence of environmental factors on primate behavior during dispersal<\/a><\/p>\n\n\n\n

Pierre Hauptmann: Time Series Analysis and Prediction for Hourly Bike Rentals Using LSTM Networks<\/a><\/p>\n\n\n\n

Tanguy Linard de Guertechin: Clustering Of Roads In Vaud, Switzerland<\/a><\/p>\n\n\n\n

Sadou Abdelmadjid: Predicting Population Flows in the US Using Deep Learning<\/a><\/p>\n<\/details>\n\n\n\n

<\/p>\n\n\n\n

Fall 2023<\/summary>\n

Chongguang Fan: Large area forest aboveground biomass mapping by integrating GEDI and Sentinel-2<\/a><\/p>\n\n\n\n

Ayoub Fatihi: Mapping of Geological Structures: A CNN Approach<\/a><\/p>\n\n\n\n

Michail Henry: Convolutional neural network for seismic wave classification<\/a><\/p>\n\n\n\n

Yvan Martinet: Classifying fossil tracks with neural networks<\/a><\/p>\n\n\n\n

Missilene Stolf: Predicting tornadoes\u2019 fatalities in the USA: A combined approach using both classification and regression<\/a><\/p>\n\n\n\n

Diksha Acharya: Anomaly detection of extreme temperatures and precipitation in India using ERA5 Land dataset<\/a><\/p>\n\n\n\n

Sarah Borotau Fons: Machine Learning for Earthquake Prediction from Earthquake Catalogues – Comparison of 3 different methods<\/a><\/p>\n\n\n\n

Mojan Marghoub Shadkar: Classifying water safety through machine learning algorithms<\/a><\/p>\n\n\n\n

Petra Tomurad: Water quality prediction<\/a><\/p>\n<\/details>\n\n\n\n

Fall 2022<\/summary>\n
\n

Marc O’Callaghan: Comparison of YOLOv4-tiny and YOLOv7-tiny for instream wood detection<\/a><\/p>\n\n\n\n

Julia Walker: Comparing Machine Learning Algorithms for Land Cover Classification for Vallon de Nant, Switzerland<\/a><\/p>\n\n\n\n

Fabien Augsburger: Machine Learning Prediction of Moderate and Extreme Wind Bursts in Europe<\/a><\/p>\n\n\n\n

\u00c9tienne Delaloye: Using a Random Forest Classifier and Logistic Regression for Binary Classification of Water Potability<\/a><\/p>\n\n\n\n

Axelle Bersier: Exploratory analysis of clustering Swiss population using Self-Organizing Maps<\/a><\/p>\n\n\n\n

Margaux Hofmann: Predicting Snow Line Elevation and Glacier Activity using Machine Learning<\/a><\/p>\n\n\n\n

Elias Al Alam: Detection of Oil Spills Using Machine Learning<\/a><\/p>\n\n\n\n

Doruntina Bekolli: Prediction of Rainfall in Australia Using Logistic Regression and Random Forest Algorithms<\/a><\/p>\n\n\n\n

Douglas Stumpp: Extracting road networks from satellite images with neural networks to contribute to risk mitigation<\/a><\/p>\n\n\n\n

Haakon Vikesaa: Comparing Logistic Regression and Random Forest Classifiers for Landslide Hazard Assessment in Vaud, Switzerland<\/a><\/p>\n\n\n\n

Melinda Femminis: Quantitative estimation of the area affected by the 2022 Jagersfontain tailings dam\u2019s collapse<\/a><\/p>\n\n\n\n

Emmanuel Emezina: Assessment of the EuroSAT Dataset by applying Deep Learning Techniques<\/a><\/p>\n\n\n\n

Florent Rouge: Instream wood detection using the YOLOv4 algorithm on aerial images of the Sp\u00f6l river<\/a><\/p>\n\n\n\n

Max Henking: Classifying train delays based on environmental variables<\/a><\/p>\n\n\n\n

J\u00e9r\u00e9mie Fragni\u00e8re: Clustering Tropical Cyclogenesis Events Based on Environmental Predictors and Oceanic Basins<\/a><\/p>\n\n\n\n

Christophe Reymond: Instream wood detection using the YOLOv4 object detection algorithm<\/a><\/p>\n\n\n\n

Faye Perchanok: Predicting proglacial lake discharge using machine learning algorithms<\/a><\/p>\n\n\n\n

Jonathan Cotasson: Comparing algorithms for storm cluster detection<\/a><\/p>\n\n\n\n

Janbiro Ntamushobora: Tracking the evolution of gentrification in the neighborhoods of Paris<\/a><\/p>\n\n\n\n

Baptiste Poffet: Quantifying deforestation in the Amazon forest using machine learning<\/a><\/p>\n\n\n\n

Thomas Krieger: Predicting extreme events with Random Forest and K-Nearest Neighbors<\/a><\/p>\n<\/div><\/div>\n<\/details>\n\n\n\n

Spring 2022<\/summary>\n
\n

Isabel Nicholson Thomas: Can machine learning be used to generate accurate and regular Land Cover data for Switzerland from satellite imagery?<\/a><\/p>\n\n\n\n

Christophe Reis: Predicting the exposure concentration of a pollutant on tadpoles based on their behavior and physical traits<\/a> <\/p>\n\n\n\n

Alessio Poloni: Ranking Features for Landslide Prediction<\/a><\/p>\n\n\n\n

Tabea Cache: Coupling stochastic rainfall images and data-driven flood emulation for fast urban flood mapping<\/a><\/p>\n\n\n\n

Yasmin Ghadyani: Deriving Lake Urmia water depth using Landsat 8 imagery and field measurements<\/a><\/p>\n\n\n\n

Jo\u00ebl Davila Estevez: Estimating the Hazardousness of Urban Waters from the Pollutant Concentrations<\/a><\/p>\n\n\n\n

Luca Eiholzer: Comparing performance of Random Forest and SVM for shallow landslides identification<\/a><\/p>\n\n\n\n

Gabriel Juri A.: Benchmarking imputation methods on the implementation of GRU to forecast Mendota lake\u2019s epilimnetic phosphorus<\/a><\/p>\n\n\n\n

Jeremy Keller: Zooplankton images classification using machine learning<\/a><\/p>\n\n\n\n

Josephine S. Kramer: Trace and evaluation of the dependencies of Radon222 on air pressure and temperature using machine learning tools<\/a><\/p>\n\n\n\n

Renaud Nasch: Micropollutants assessment of Vidy Bay backed by machine learning algorithms<\/a><\/p>\n\n\n\n

Aldo Fornari: Instream LargeWood detection trough YOLOV4<\/a><\/p>\n\n\n\n

Vincenzo Guzzardi: Prediction of Ammonium concentration in a river using unsupervised Machine Learning<\/a><\/p>\n\n\n\n

Alessandro Giovanardi: Suspended bedload in a proglacial river<\/a><\/p>\n\n\n\n

Keyvan Diba: Using machine learning regarding suspended sediment concentration in a proglacial river<\/a><\/p>\n<\/div><\/div>\n<\/details>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1002254,"featured_media":1044,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"templates\/template-cover.php","meta":{"_seopress_robots_primary_cat":"","_seopress_titles_title":"","_seopress_titles_desc":"","_seopress_robots_index":"","footnotes":""},"class_list":["post-980","page","type-page","status-publish","has-post-thumbnail"],"_links":{"self":[{"href":"https:\/\/wp.unil.ch\/dawn\/wp-json\/wp\/v2\/pages\/980","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp.unil.ch\/dawn\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/wp.unil.ch\/dawn\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/dawn\/wp-json\/wp\/v2\/users\/1002254"}],"replies":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/dawn\/wp-json\/wp\/v2\/comments?post=980"}],"version-history":[{"count":5,"href":"https:\/\/wp.unil.ch\/dawn\/wp-json\/wp\/v2\/pages\/980\/revisions"}],"predecessor-version":[{"id":3788,"href":"https:\/\/wp.unil.ch\/dawn\/wp-json\/wp\/v2\/pages\/980\/revisions\/3788"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/dawn\/wp-json\/wp\/v2\/media\/1044"}],"wp:attachment":[{"href":"https:\/\/wp.unil.ch\/dawn\/wp-json\/wp\/v2\/media?parent=980"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}