Machine Learning

Machine Learning (ML) is part of my everyday activities. I started the endeavor in 2017 with a ML camp at DHI where we learned the fundamentals and applied on various environmental applications. I evolved from using simple regression models to more complex deep neural networks such as GANs. llllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll..loading 90%

(2024) I implemented a genetic programming algorithm to predict dune equilibrium dimensions. New predictors outperform classic predictors in the litterature, see in DUNAMICS

(2018) Time series of water levels in this Danish lagoon are incomplete due to sensor problems and weather conditions. Using a Multi Layer Perceptron, I was able to reconstruct time series of water levels for two stations in the lagoon.

(2025-2026) I am applying Random Forest algorithms to predict sedimentation, erosion rates, and mean depth evolution in various areas of the Danish Harbor of Hvide Sande.

(2019) Using a Multi Layer Perceptron, I was able to replicate sediment transport rates generated by the DHI sand transport routine (STP) routine with good accuracy. This helps to reduce computing times by avoiding running the STP tables.

(2025-2026) In this application, I am using Generative Adversarial Networks (GAN) to downscale wind and pressure fields over various geographical areas and thereby augmenting the resolution by a factor ten.