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

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 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.

Modeling- and Simulation-Driven Methodology for the Deployment of an Inland Water Monitoring System

“…The integration stage of our development process starts with the initial model, which was introduced during the design phase and which matures into a sophisticated coupled DEVS model as depicted in Figure 7. Other, more complete models may include warning and forecasting services, such as those shown in [12]. This model now encapsulates all the necessary subsystems to effectively monitor an HACB and employs a trainable inference service that guides an instrumented USV.…”

“…The effective fusion of these data streams is crucial for developing accurate and comprehensive predictive models. Additionally, there is a need for more sophisticated early warning systems that not only detect the presence of HACBs but also predict their movement and growth dynamics, enabling preemptive actions to be taken to protect public health and the environment [12].…”

“…• HACB modeling: modeling of water bodies and inference of HACB biodynamics, with a segment of the team comprising biologists [12]; • Unmanned surface vehicles (USVs): guidance, navigation, and control (GNC) of USVs [13] and sensor instrumentation.…”

A data-driven approach of layout evaluation for electric vehicle charging infrastructure using agent-based simulation and GIS

“…Other studies using simulation to investigate the spread of infectious diseases can be found in the work by Abadeer et al, 49 as well as the review article by Ayadi et al 50 Iskandar et al 51 developed an agent-based model that incorporates realistic human behavior and urban conditions to simulate pedestrian evacuation during earthquakes at the city scale, revealing the impact of debris and human behavior on population safety and the limited capacity of open spaces to provide shelters in Beirut, Lebanon. Risco-Martin et al 52 introduced a discrete-event simulation for real-time monitoring and management of harmful algal and cyanobacterial blooms (HABs), addressing the need for efficient detection and response to HABs threatening water quality in dynamic environments. The application of simulation-based methodology in emergency healthcare can be seen in the review article by Sahlaoui et al 53 Similar to the EVCI location problem in this paper, these studies use simulation methods to explore real-world issues in urban environments from temporal and spatial perspectives.…”

A Bleeding Edge Web Application for Early Detection of Cyanobacterial Blooms

“…Moreover, the paper investigates the practical implementation of the IoT-based EWS, discussing the design considerations, sensor technologies, and communication protocols essential for seamless data integration. Besides the simulations, the web application allows for the user to design, monitor and control the deployment [6] of UAVs in real environments. Moreover, the architecture allows transparent and progressive integration of real sensors and hardware, which facilitates development and makes the platform more flexible and robust.…”