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Harmful Algal and Cyanobacterial Blooms (HABs), occurring in inland andmaritime waters, pose threats to natural environments by producing toxins thataffect human and animal health. In the past, HABs have been assessed mainly bythe manual collection and subsequent analysis of water samples and occasionallyby automatic instruments that acquire information from fixed locations. Theseprocedures do not provide data with the desirable spatial and temporalresolution to anticipate the formation of HABs. Hence, new tools andtechnologies are needed to efficiently detect, characterize and respond to HABsthat threaten water quality. It is essential nowadays when the world's watersupply is under tremendous pressure because of climate change,overexploitation, and pollution. This paper introduces DEVS-BLOOM, a novelframework for real-time monitoring and management of HABs. Its purpose is tosupport high-performance hazard detection with Model Based Systems Engineering(MBSE) and Cyber-Physical Systems (CPS) infrastructure for dynamicenvironments.

Simulation-driven engineering for the management of harmful algal and cyanobacterial blooms