Evaluation of Machine Learning Predictions of a Highly Resolved Time Series of Chlorophyll-a Concentration


Contact
johannes.rick [ at ] awi.de

Abstract

Pelagic chlorophyll-a concentrations are key for evaluation of the environmental status and productivity of marine systems, and data can be provided by in situ measurements, remote sensing and modelling. However, modelling chlorophyll-a is not trivial due to its nonlinear dynamics and complexity. In this study, chlorophyll-a concentrations for the Helgoland Roads time series were modeled using a number of measured water and environmental parameters. We chose three common machine learning algorithms from the literature: the support vector machine regressor, neural networks multi-layer perceptron regressor and random forest regressor. Results showed that the support vector machine regressor slightly outperformed other models. The evaluation with a test dataset and verification with an independent validation dataset for chlorophyll-a concentrations showed a good generalization capacity, evaluated by the root mean squared errors of less than 1 µg L−1. Feature selection and engineering are important and improved the models significantly, as measured in performance, improving the adjusted R2 by a minimum of 48%. We tested SARIMA in comparison and found that the univariate nature of SARIMA does not allow for better results than the machine learning models. Additionally, the computer processing time needed was much higher (prohibitive) for SARIMA. Keywords: time series regression; artificial intelligence; Helgoland Roads time series; support vector machine; multi-layer perceptron; random forest; productivity; SARIMA



Item Type
Article
Authors
Divisions
Primary Division
Programs
Primary Topic
Helmholtz Cross Cutting Activity (2021-2027)
N/A
Research Networks
Publication Status
Published
Eprint ID
54524
DOI 10.3390/app11167208

Cite as
de Luca Lopes de Amorim, F. , Rick, J. , Lohmann, G. and Wiltshire, K. H. (2021): Evaluation of Machine Learning Predictions of a Highly Resolved Time Series of Chlorophyll-a Concentration , Applied Science, 11 (16) . doi: 10.3390/app11167208


Share
Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email


Citation

Geographical region

Research Platforms

Campaigns
N/A


Actions
Edit Item Edit Item