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

TL;DR

This paper presents DEVS-BLOOM, a novel real-time monitoring framework utilizing Model Based Systems Engineering and Cyber-Physical Systems to improve detection and management of harmful algal and cyanobacterial blooms, addressing current limitations in spatial and temporal data resolution.

Contribution

The paper introduces DEVS-BLOOM, a new simulation-driven framework that enhances real-time HABs detection and management using MBSE and CPS technologies.

Findings

Improved spatial and temporal resolution in HAB monitoring.

Enhanced real-time hazard detection capabilities.

Supports dynamic environmental management.

Abstract

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 DEVS-BLOOM, a novel framework for real-time monitoring and management of HABs. Its purpose is to support high-performance hazard detection with Model Based Systems Engineering (MBSE) and Cyber-Physical Systems (CPS) infrastructure for dynamic environments.