Author(s)

Italo F. Di Paolo^1*, Nelson A. Gouveia², Luiz C. Ferreira Neto³, Eduardo T. Paes⁴, Nandamudi L. Vijaykumar², Ádamo L. Santana³

¹Center for Natural Sciences and Technology (CCNT), Pará State University (UEPA), Belém, Brazil.
²National Institute for Space Research (INPE), São José dos Campos, Brazil.
³Institute of Technology (ITEC), Federal University of Pará (UFPA), Belém, Brazil.
⁴Socio-Environmental Institute and Water Resources (ISARH), Federal Rural University of Amazonia (UFRA), Belém, Brazil.

The contemporary scientific literature that deals with the dynamics of marine chlorophyll-a concentration is already customarily employing data mining techniques in small geographic areas or regional samples. However, there is little focus on the issue of missing data related to chlorophyll-a concentration estimated by remote sensors. Intending to provide greater scope to the identification of the spatiotemporal distribution patterns of marine chlorophyll-a concentrations, and to improve the reliability of results, this study presents a data mining approach to cluster similar chlorophyll-a concentration behaviors while implementing an iterative spatiotemporal interpolation technique for missing data inference. Although some dynamic behaviors of said concentrations in specific areas are already known by specialists, systematic studies in large geographical areas are still scarce due to the computational complexity involved. For this reason, this study analyzed 18 years of NASA satellite observations in one portion of the Western Atlantic Ocean, totaling more than 60 million records. Additionally, performance tests were carried out in low-cost computer systems to check the accessibility of the proposal implemented for use in computational structures of different sizes. The approach was able to identify patterns with high spatial resolution, accuracy and reliability, rendered in low-cost computers even with large volumes of data, generating new and consistent patterns of spatiotemporal variability. Thus, it opens up new possibilities for data mining research on a global scale in this field of application.

Data Mining, Clustering, Chlorophyll, Atlantic, Missing Data, Small Hardware

Di Paolo, I. , Gouveia, N. , Neto, L. , Paes, E. , Vijaykumar, N. and Santana, Á. (2019) Data Mining of Spatio-Temporal Variability of Chlorophyll-a Concentrations in a Portion of the Western Atlantic with Low Performance Hardware. Journal of Software Engineering and Applications, 12, 149-170. doi: 10.4236/jsea.2019.125010.