Harmful algal and cyanobacterial blooms (HABs), occurring in inland and maritime waters, pose threats to natural environments by producing toxins that affect human and animal health. In the past, HABs have been assessed mainly by the manual collection and subsequent analysis of water samples and occasionally by automatic instruments that acquire information from fixed locations. These procedures do not provide data with the desirable spatial and temporal resolution to anticipate the formation of HABs. Hence, new tools and technologies are needed to efficiently detect, characterize and respond to HABs that threaten water quality. It is essential nowadays when the world’s water supply is under tremendous pressure because of climate change, overexploitation, and pollution. This paper introduces Discrete Event System Specification-BLOOM, a novel framework for real-time monitoring and management of HABs. Its purpose is to support high-performance hazard detection with model-based system engineering and cyber-physical systems infrastructure for dynamic environments.
VincentWF. Cyanobacteria. In: LikensGE (ed.) Encyclopedia of inland waters. Vol. 3. London: Academic, 2009, pp. 226–232.
2.
SchmaleDGAultAPSaadW, et al. Perspectives on harmful algal blooms (HABs) and the cyberbiosecurity of freshwater systems. Front Bioeng Biotechnol2019; 7: 128.
3.
MeriluotoJSpoofLCoddGA. Handbook of cyanobacterial monitoring and cyanotoxin analysis. John Wiley & Sons: Hoboken, NJ, 2017, p. 576.
4.
UngMTMoellmerWO. A practical example of the use of simulation to develop an optimal plan for water—quality control. Simulation1972; 19: 109–117.
WuYZhangJHouZ, et al. Seasonal dynamics of algal net primary production in response to phosphorus input in a mesotrophic subtropical plateau lake, Southwestern China. Water2022; 14: 835.
ZeiglerBPMuzyAKofmanE. Theory of modeling and simulation: discrete event & iterative system computational foundations. Cambridge, MA: Academic Press, 2018.
11.
MarcosigEPGiribetJICastroR. DEVS-OVERROS (DOVER): a framework for simulation-driven embedded control of robotic systems based on model continuity. In: 2018 winter simulation conference (WSC), Gothenburg, 9–12 December 2018, pp. 1250–1261. New York: IEEE.
12.
Vinçon-LeiteBCasenaveC. Modelling eutrophication in lake ecosystems: a review. Sci Total Environ2019; 651: 2985–3001.
13.
Van SebilleEGriffiesSMAbernatheyR, et al. Lagrangian ocean analysis: fundamentals and practices. Ocean Modelling2018; 121: 49–75.
14.
ChenYSongLLiuY, et al. A review of the artificial neural network models for water quality prediction. Appl Sci2020; 10: 5776.
15.
RoussoBZBertoneEStewartR, et al. A systematic literature review of forecasting and predictive models for cyanobacteria blooms in freshwater lakes. Water Res2020; 182: 115959.
VélezCAlfonsoLSánchezA, et al. Centinela: an early warning system for the water quality of the Cauca river. J Hydroinformatics2014; 16: 1409–1424.
19.
SilvaAPintoLRodriguesS, et al. A HAB warning system for shellfish harvesting in Portugal. Harmful Algae2016; 53: 33–39.
20.
SerramiáA. SAIH/SAICA: automatic hydrological information system and automatic water quality information system in the Spanish watersheds. Tec Ambient2004; 139: 15–18.
BashaEARavelaSRusD. Model-based monitoring for early warning flood detection. In: Proceedings of the 6th ACM conference on embedded network sensor systems, SenSys 2008, Raleigh, NC, 5–7 November 2008, pp. 295–308. New York: Association for Computing Machinery.
25.
CurlJMNadingTHeggerK, et al. Digital twins: the next generation of water treatment technology. J Am Water Works Ass2019; 111: 44–50.
26.
HenaresKRisco-MartínJLAyalaJL, et al. Efficient micro data centres deployment for mobile healthcare monitoring systems in iot urban scenarios. J Simul2022; 16: 589–603.
27.
CárdenasRArrobaPRisco-MartínJL. Bringing AI to the edge: a formal M&S specification to deploy effective IoT architectures. J Simul2021; 16: 494–511.
28.
Risco-MartínJLMittalSHenaresK, et al. XDEVS: a toolkit for interoperable modeling and simulation of formal discrete event systems. Software Prac Exp2022; 53: 748–789.
29.
Risco-MartínJLHenaresKMittalS, et al. A unified cloud-enabled discrete event parallel and distributed simulation architecture. Simul Model Pract Theor2022; 118: 102539.
30.
Herguedas-PinedoBRisco-MartínJLEstebanS, et al. Predictive modeling and simulation system for the management of harmful cyanobacteria blooms. In: Proceedings of the 2023 annual modeling and simulation conference (ANNSIM’23), Hamilton, ON, Canada, 23–26 May 2023.
NiyonkuruDWainerG. A DEVS-based engine for building digital quadruplets. Simulation2021; 97: 485–506.
33.
Ferrero-LosadaSBesada-PortasERisco-MartínJL, et al. DEVS-based modeling and simulation of data-driven exploration algorithms of lentic water bodies with an ASV. In: Proceedings of the 2023 annual modeling and simulation conferenceNNSIM’23), Hamilton, ON, Canada, 23–26 May 2023.
