Some remarks about a community open source Lagrangian pollutant transport and dispersion model

Abstract

Nowadays fishes and mussels farming is very important, from an economical point of view, for the local social background of the Bay of Naples. Hence, the accurate forecast of marine pollution becomes crucial to have reliable evaluation of its adverse effects on coastal inhabitants' health. The use of connected smart devices for monitoring the sea water pollution is getting harder because of the saline environment, the network availability and the maintain and calibration costs2. To this purpose, we designed and implemented WaComM (Water Community Model), a community open source model for sea pollutants transport and dispersion. WaComM is a model component of a scientific workflow which allows to perform, on a dedicated computational infrastructure, numerical simulations providing spatial and temporal high-resolution predictions of weather and marine conditions of the Bay of Naples leveraging on the cloud based31FACE-IT workflow engine27. In this paper we present some remarks about the development of WaComM, using hierarchical parallelism which implies distributed memory, shared memory and GPGPUs. Some numerical details are also discussed. Peer-review under responsibility of the Conference Program Chairs

Coastal Marine Data Crowdsourcing Using the Internet of Floating Things: Improving the Results of a Water Quality Model

“…Data are homogenized using the Signal K format by the Data Talkers component, which handles the parsing of most part of NMEA0183, 9 NMEA2000 over NMEA0183 $PCDIN, 10 and SeaTalk1 over NMEA0183 $STALK 11 sentences. The Data Talkers are bi-directional components because it also acts as data repeater or data forwarder on the same or different interface.…”

“…In WaComM, several basic algorithms have been optimized and, in order to improve its performance on a High Performance Computing environment, some features like restarting and parallelization techniques in shared memory environments have been added. In the following, we describe the underlying mathematical model as explained in [10].…”

“…As mitigation actions, the following have been considered: (i) planning of periodical in-situ measurements [6]; (ii) using numerical model simulations [7]; (iii) using connected smart devices for real-time monitoring [8]; (iv) a mixing of the previous solutions in the IoFT context [9], [10].…”

Workflow-based automatic processing for Internet of Floating Things crowdsourced data

“…The rapid spread of Internet of Things (IoT) technologies has greatly increased the use of geographic data [1]. In the work reported here, we leverage this trend to develop a new approach to the problem of obtaining high resolution 3-D maps of the sea floor, as required, for example, to model the impact of sea storms on human coastal activities [2,3,4], the diffusion and dispersion of sea pollutants [5,6,7], and the drift of floating objects, as required for safety at sea [8]. We show how to use crowdsourced data [9] from leisure boats to create unique and dense sea bathymetry datasets that, due to the large numbers of such vessels, can be particularly detailed in shallow waters and coastal areas.…”