###### 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 ug L\({}^{-1}\). Feature selection and engineering are important and improved the models significantly, as measured in performance, improving the adjusted R\({}^{2}\) 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.  
time series regression; artificial intelligence; Helgoland Roads time series; support vector machine; multi-layer perceptron; random forest; productivity; SARIMA +
Footnote †: journal: Medical  
successful application in many fields of science and to a great variety of problems. The focus is often placed on tackling pattern recognition problems and on the construction of predictive models to make data-driven decisions [12]. According to [13], the general benefits of ML algorithms for time series prediction over classical methods include the ability of supporting noisy features, noise and complexity in the relationships between variables and in the handling of irrelevant features.  
State-of-the-art ML algorithms for time series regression include random forest regressor (RF), support vector machine regressor (SVR) and neural networks multi-layer perceptron regressor (MLP). All of these have been used to some degree in the literature for the prediction of chlorophyll-a concentrations in aquatic systems, and have achieved significantly accurate results in both error and goodness of fit metrics [3; 11; 14]. These are studies based in chl-a time series either with short length and daily frequency or long-term, low frequency sampling time series, using different ML methods to best predict chl-a behavior. The features applied as predictors in these studies are limited to just a few, but it must be considered that the dynamics in lacustrine systems are distinct from those presented in marine systems. Here we extend these ideas and test these methods on a good quality long-term time series, the Helgoland Roads time series, evaluating the prediction using unseen data. With the purpose to compare ML methods with a classical statistical regression model, we included an improved autoregressive integrated moving average (ARIMA) model, called seasonal ARIMA (SARIMA), which includes seasonal parameters to support data with a seasonal component [15].  
The objective of this work is to evaluate the accuracy of machine learning algorithms for the estimation of chlorophyll-a concentration, using in situ high resolution long-term datasets. We (1) assess three ML algorithms--random forest, support vector regressor and neural networks multi-layer perceptron regressor--for chlorophyll-a concentration estimation; (2) examine the importance of feature selection and engineering in the different models; (3) compare with, and evaluate, a univariate SARIMA classical regression model.