Algal Bloom Forecasting

Classical Machine Learning versus Deep-Learning

Bachelor thesis (2023)

Authors

R.A.X.M. Lubbers Electrical Engineering, Mathematics and Computer Science

Contributors

A. Lengyel (mentor)
J.C. van Gemert Pattern Recognition and Bioinformatics - EEMCS (mentor)
R. Bruintjes Pattern Recognition and Bioinformatics - EEMCS (mentor)
K.G. Langendoen Embedded Systems - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Rob Lubbers
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Published Date

03-02-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

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.