## 5 Conclusions  
In this work, we evaluated three machine learning algorithms in a regression task. Support vector regressor presented a slightly better performance, with the advantage that it used less computational time, and generated chlorophyll concentration predictions with 0.78 correlation to the observed data, in comparison to 0.76 and 0.75 for MLP and RF, respectively. Moreover, the root mean square error was approximately 1.1 ug L\({}^{-1}\) for the test dataset and less than one for the independent validation data, which is approximately 38% percent smaller than the standard deviation of 2.9 ug L\({}^{-1}\). This study demonstrates the ability of machine learning models to use environmental in situ time series to predict the chlorophyll concentration with significant accuracy (R\({}^{2}\)), higher than 70%, and the importance of tuning hyperparameters and defining the best predictors (feature selection). Most chlorophyll-a prediction studies are conducted in fresh water environments or using satellite data and limited time series, so this work can be considered a step toward the use of machine learning algorithms in marine areas based on long-term time series. Being aware of the limitations presented in this study, in future works it would be interesting to work with irregular sampled time series, improve the method for feature selection, ensemble results of different ML and classical statistical models, and evaluate the forecasting power of these models in the short and long term. Besides, the use of deep learning approacheshas become more and more common, and in many cases, they are outperforming the traditional ML algorithms, with the cost of higher computer processing times.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11167208/s1](https://www.mdpi.com/article/10.3390/app11167208/s1)]([https://www.mdpi.com/article/10.3390/app11167208/s1](https://www.mdpi.com/article/10.3390/app11167208/s1)), Figure S1: Time series of parameters used to predict chlorophyll-a concentration: (a) Secchi disk depth, in meters (m); (b) Sea Surface Temperature, in degrees Celsius (\({}^{\circ}\)C); (c) Salinity (\(-\)); (d) Silicate (\(\mu\)mol L\({}^{-1}\)); (e) Phosphate (\(\mu\)mol L\({}^{-1}\)); (f) Nitrate (\(\mu\)mol L\({}^{-1}\)); (g) Sunlight duration, in hours (h); (h) NAO index (\(-\)); (i) Wind Direction, in degrees (\({}^{\circ}\)); (j) Wind Speed, in meters per second (m s\({}^{-1}\)); and (l) Total zooplankton abundance, individuals per cubic meter (ind. m\({}^{-3}\)).  
Conceptualization, F.d.L.L.d.A.; methodology, F.d.L.L.d.A.; formal analysis, F.d.L.L.d.A.; data curation, J.R.; writing--original draft preparation, F.d.L.L.d.A.; writing--review and editing, G.L., J.R. and K.H.W.; visualization, F.d.L.L.d.A.; supervision, G.L. and K.H.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from [PERSON] ([EMAIL_ADDRESS]). The data are not publicly available, for our archives and financial agencies we have to document to whom and which time series data have been made available.  
We acknowledge the present and past crews of the research vessels Aade and Ellenbogen of the Biologische Anstalt Helgoland (BAH) for their unfailing provision of samples. We also thank [PERSON], [PERSON], [PERSON] and all colleagues of time series group and those who were instrumental in the analysis of samples during the last decades. We thank the NOAA Physical Sciences Laboratory and Deutsche Wetterdienst for providing climate and meteorological data. We acknowledge support by the Open Access Publication Funds of Alfred-Wegener-Institut Helmholtz-Zentrum fur Polar- und Meeresforschung. We thank the Editor and two anonymous reviewers for their careful evaluation of our manuscript, the positive feedback, and their comments that helped to further improve the manuscript.  
The authors declare no conflict of interest.