# Multivariate Relationship in Big Data Collection of Ocean Observing System
## 4 Discussion and Conclusions  
This paper investigates a deep learning framework for the prediction of low-frequency sampled variables (such as nutrient concentrations and carbonate systems variables) starting from high-frequency sampled ones (specifically: salinity, and oxygen) and ancillary information such as time and geolocation of the sampling. The method was already proposed [10; 11; 12; 13; 14; 15], and we applied some improvements specifically for the Mediterranean Sea: a larger training dataset [18], a new two-step quality check routine to improve the dataset and a confidence interval relative to the prediction, exploiting the concept of deep ensembles of neural networks. The method was then accurately validated, and the results show a significant improvement with respect to the state-of-the-art, for all the (fitness) metrics and for all the variables. The improvement in results over previous applications [15] is due to several factors. First, the dataset used is characterized by a larger amount of samples and wider coverage of the Mediterranean Sea area. Even if this would be not a surprising aspect, it is important to note that given the nature of marine data (i.e., sparse and spatially biased, noisy and potentially with inconsistencies), it was not a given result. The dataset has been further improved by the two-step quality check routine that leads to the removal of noisy and unreliable samples. During the whole process, our goal was to find an equilibrium between the necessity to delete problematic data and, at the same time, avoid the removal of cruises that lead to important information for the prediction. To ensure the reliability of our method, we randomly checked some samples extracted from cruises that our method rejected. Particular attention, during this process, has been paid to avoid introducing a bias in the spatial distribution of the sample for the different areas of the Mediterranean Sea. This is achieved by stopping the iteration of the check before a bias in the spatial distribution appeared. Our quality check procedure can also be applied to other assembling datasets (or data collection such as Emodnet ones [21]) where multiple sources are merged and possible standardization, conversion and transcription errors can go unnoticed. Furthermore, the deep learning architecture has been modified (e.g., nonlinear functions between layers, the number of neurons per layer, the optimization algorithm for training, and so on) in order to improve the prediction performance. Finally, thanks to the information stored in the _EMODnet_ dataset, we tested the prediction of two additional variables not previously investigated: chlorophyll-a and ammonium (NH\({}_{4}^{+}\)). Results for these variables are satisfactory, allowing us to further the potentiality of our application. We would also like to underline that we obtained a reduction of the fitness with a faster method, which requires far fewer computational resources compared to the one introduced in [15], which is based on a Bayesian framework. In fact, we observed that training our model with a Bayesian architecture (not shown) instead of the non-Bayesian one did not lead to an improvement in the prediction performance. This is a consequence of the bigger (and quality-checked) dataset used for the training. This model can prove useful for several reasons. First, a possible application is to infer values of carbonate system variables and nutrients starting from samples collected through BGC-Argo float sensors (when they are equipped with the oxygen sensor). Given the cost of the other biogeochemical sensors [7], this represents a step towards the further exploitation of this observing system. Secondly, model predictions can be used for real-time quality checks of raw variables effectively observed by full-equipped BGC-Argo floats.