# Multivariate Relationship in Big Data Collection of Ocean Observing System
## 4 Discussion and Conclusions
At present, the quality check of BGC-Argo variables relies on classic statistical procedures [31], while our model prediction can provide a further and easy-to-computecomparison term to spot inconsistencies and biases in these data. This will allow us to have a richer and more detailed knowledge of the Mediterranean Sea basin, which is essential information, especially to understand how the marine environment is changing as a result of the anthropic impact. In any case, as pointed out in Section 3.1, MLP reconstructions denote the inability in the generation of a smooth and regular curve. In fact, marine variables are not only characterized by specific values of concentration, but also typical shapes of the profiles that inform about ongoing processes and dynamics [32; 33]. Thus, the addition of information on the shape of profiles, such as typical patterns in image reconstruction, can be an important element to be added. So, a possible improvement can be to test an approach based on convolutional deep learning architecture [34; 35] to reconstruct nutrient profiles from information such as sampling time, geolocation, and profiles of temperature, salinity, and oxygen. Thus, spatial-aware architecture could overcome such issues and lead to the generation of smooth predicted profiles. The Mediterranean Sea is characterized by significant physical and biogeochemical gradients at different spatial scales [36; 37]. The uneven coverage of the data impacted the capability of the network to predict the large range of variability of the variables. Indeed, our results (Figure 4) showed the presence of potential biases linked to local unresolved variability. Therefore, it would be interesting to investigate if introducing a restriction on the investigated areas can provide more precise results. However, while the restriction of the area allows having more data over more homogeneous variability, it can also reduce the number of available data for the training below a safe limit. Our results confirm the previous evidence of [10; 14; 15], namely that some biogeochemical proprieties can be successfully predicted by neural networks using temperature, salinity, oxygen, and geolocalization. The rationale lies in the fact that the same processes (e.g., transport and biogeochemical processes) concurrently shape the spatial patterns of different variables. The choice of the predictive variables is not the result of an optimization process, but reflects the fact that those variables are the most common and less expensive sensors in the Argo platforms [31; 38]. It would be interesting to take the idea further and test the goodness of fit even when only the temperature and salinity are considered. While it is reasonable to expect that prediction power would decrease, the number of available floats would increase at least 5-fold. [PERSON], [PERSON] and L.M contributed to the study conception and design. Material preparation, data collection and analysis were performed by [PERSON] The first draft of the manuscript was written by [PERSON] and all authors commented on previous versions of the manuscript. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The following data sources were used: Mediterranean Sea-Eutrophication and Acidity aggregated datasets 1911/2020 v2021. This resource was generated in the framework of EMODnet Chemistry, under the support of DG MARE Call for Tender EASME/2019/OP/0003-lot4. Use limitation: CC BY 4.0: [[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)), accessed on 12 March 2023. Use constraints: unrestricted. doi:10.6092/ep6n-tp63 BGC-ARGO. Argo (2000). Argo float data and metadata from Global Data Assembly Centre (Argo GDAC). SEANOE. doi:10.17882/42182. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: