Algal Bloom Forecasting

Algal Bloom Forecasting: Classical Machine Learning versus Deep-Learning

Lubbers, Rob (TU Delft Electrical Engineering, Mathematics and Computer Science)

Lengyel, A. (mentor)
van Gemert, J.C. (mentor)
Bruintjes, R. (mentor)
Langendoen, K.G. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2023-02-03

The aim of this paper is to find out which Machine Learning (ML) model predicts the concentration of Chlorophyll-a, in the Palmar lake in Uruguay best. Currently there are no such models to predict the growth in this lake. The algorithms which will be compared in this paper are a Linear Regression model and the U-Net model. We will compare the losses of the two models to determine which algorithm performs best. The less loss a model has, the more accurate it is, and thus the better it is. The loss of the U-Net model failed to converge to a value, therefore it was impossible to compare the two models.

U-Net
Machine Learning
Forecasting

http://resolver.tudelft.nl/uuid:3781e31e-9cfc-4ace-8b35-ea08172171ba

Student theses

bachelor thesis

© 2023 Rob Lubbers