Algal Bloom Forecasting
Classical Machine Learning versus Deep-Learning
R.A.X.M. Lubbers (TU Delft - Electrical Engineering, Mathematics and Computer Science)
A. Lengyel – Mentor
J.C. van Gemert – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
R. Bruintjes – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
Koen Langendoen – Graduation committee member (TU Delft - Embedded Systems)
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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.