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The impact of machine learning algorithms on everyday life is overwhelming until the novel concept of datacracy as a new social paradigm. In the field of computational environmental science and, in particular, of applications of large data science proof of concept on the natural resources management this kind of approaches could make the difference between species surviving to potential extinction and compromised ecological niches. In this scenario, the use of high throughput workflow engines, enabling the management of complex data flows in production is rock solid, as demonstrated by the rise of recent tools as Parsl and DagOnStar. Nevertheless, the availability of dedicated computational resources, although mitigated by the use of cloud computing technologies, could be a remarkable limitation. In this paper, we present a novel and improved version of DagOnStar, enabling the execution of lightweight but recurring computational tasks on the microservice architecture. We present our preliminary results motivating our choices supported by some evaluations and a real-world use case.

Coastal geomorphology requires increasingly accurate topographic information of the beach systems to perform reliable simulation of coastal erosion and flooding phenomena, in order to obtain reliable vulnerability assessment. Among the range of remote sensing methods available to produce measurement of the coastline, this study tests the utility of low-altitude aerial imagery by Unmanned Aerial Vehicle (UAV) on the beaches of Serapo and S. Agostino, located in the Latinum region, in the Thyrrenian Sea, and spaceborne Synthetic Aperture Radar (SAR) measurements which are processed with the purpose of exploiting the dual-polarimetric information to better distinguish land from sea.The dataset consists of two sets of images, both acquired by UAV and SAR platforms, the first in December 2013 and the second in June 2018. A comparison of the coastline extraction obtained with the same technique in two different temporal sequences and between different techniques in the same period has been performed in order to assess benefits and limitations of each methodology.

Accurate prediction of trends in marine pollution is strategic, given the negative effects of low water quality on human marine activities. We describe here the computational and functional performance evaluation of a decision making tool that we developed in the context of an operational workflow for food quality forecast and assessment. Our Water Community Model (WaComM) uses a particle-based Lagrangian approach relying on tridimensional marine dynamics field produced by coupled Eulerian atmosphere and ocean models. WaComM has been developed matching the hierarchical parallelization design requirements and tested in Intel X86_64 and IBM P8 multi core environments and integrated in FACE-IT Galaxy workflow. The predicted pollutant concentration and the amount of pollutants accumulated in the sampled mussels are compared in search of coherent trends to prove the correct model behaviour. In the case study shown in this paper, the predicted Lagrangian tracers, acting as pollutant concentration surrogates, tend to spread rapidly and undergo rapid dilution as expected depending on dominant water column integrated currents.