EA-Based ASV Trajectory Planner for Pollution Detection in Lentic Waters

Carazo-Barbero, Gonzalo; Besada-Portas, Eva; Girón-Sierra, José M.; López-Orozco, J. A.

doi:10.1007/978-3-030-72699-7_51

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…These models are completely transparent to the web application and thus to the final user. The inference service supports different models, such as the predictive model that is described in [6] based on differential equations, or a particle transport model presented in [20,21] that determines Cyanobacterial Bloom (CB) distribution from the water currents and the inherent CB behavior (in particular, its biological growth and vertical displacements). In addition, the research group of the authors is currently focused on the implementation of a deep-learning model that is expected to provide more accuracy in the predictions of blooms.…”

Section: Architecture and Implementationmentioning

confidence: 99%

A Bleeding Edge Web Application for Early Detection of Cyanobacterial Blooms

Chacón,

Andrade,

Risco-Martín

et al. 2024

Electronics

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Harmful Algal and Cyanobacterial Bloom (HACB) threaten aquatic ecosystems, human health, and the economy. Many factors influence these dynamic events, which are often difficult to detect until the late stages of growth. The inclusion of an Early Warning System (EWS) can be instrumental in identifying hazards and preventing or at least minimizing their impact. Traditional monitoring approaches often fail to provide the real-time, high-resolution data needed for effective early warnings. The integration of Internet of Things (IoT) technologies offers a promising avenue to address these limitations by creating a network of interconnected sensors capable of continuously collecting and transmitting data from various aquatic environments. In this paper, we present DEVS-BLOOM-WebUI, an advanced web application that extends the capabilities of the DEVS-BLOOM framework, providing a user-friendly interface that supports different user roles. The application includes an interface to manage users and permissions, dashboards to inspect data (from sensors, Unmanned Surface Vehicles (USVs), weather stations, etc.), train AI models, explore their predictions, and facilitate decision-making through notification of early warnings. A key feature of DEVS-BLOOM-WebUI is the Scenario Configuration Editor (SCE). This interactive tool allows for users to design and configure the deployment of monitoring infrastructure within a water body, enhancing the system’s adaptability to user-defined simulation scenarios. This paper also investigates the practical implementation of an IoT-based EWS, discussing design considerations, sensor technologies, and communication protocols essential for seamless data integration and effective operation of the EWS.

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Section: Architecture and Implementationmentioning

confidence: 99%

A Bleeding Edge Web Application for Early Detection of Cyanobacterial Blooms

Chacón,

Andrade,

Risco-Martín

et al. 2024

Electronics

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“…For example, the authors in [15] describe a monitoring mission for crops using aerial vehicles using a Gaussian Process-based approach, in which different parameters are measured from different heights using onboard sensors. In [16], the authors introduce a novel path planner employing Evolutionary Algorithms (EA) for an ASV. This planner seeks to optimize the ASV trajectory and to obtain information about the distribution of cyanobacteria in water currents and the cyanobacteria behavior during the mission.…”

Section: Related Workmentioning

confidence: 99%

AquaHet-PSO: An Informative Path Planner for a Fleet of Autonomous Surface Vehicles With Heterogeneous Sensing Capabilities Based on Multi-Objective PSO

Kathen,

Samaniego,

Flores

et al. 2023

IEEE Access

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The importance of monitoring and evaluating the quality of water resources has significantly grown over time. To achieve this effectively, an option is to employ an intelligent monitoring system capable of measuring the physical and chemical parameters of water. Surface vehicles equipped with sensors for measuring water quality parameters offer a viable solution for these missions. This work presents a novel approach called AquaHet-PSO, which addresses the challenge of simultaneously monitoring multiple water quality parameters with several peaks of contamination using a heterogeneous fleet of autonomous surface vehicles. Each vehicle in the fleet possesses a different set of sensors, such as number of sensors and sensor types, which is the definition provided by the authors for a heterogeneous fleet. The AquaHet-PSO consists of three main phases. In the initial phase, the vehicles traverse the water resource to obtain preliminary models of water quality parameters. These models are then utilized in the second phase to identify potential contamination areas and assign vehicles to specific action zones. In the final phase, the vehicles focus on a comprehensive characterization of the parameters. The proposed system combines several techniques, including Particle Swarm Optimization and Gaussian Processes, with the integration of genetic algorithm to maximize the distances between the initial positions of vehicles equipped with identical sensors, and a distributed communication system in the final phase of the AquaHet-PSO. Simulation results in the Ypacarai lake demonstrate the effectiveness and efficiency of AquaHet-PSO in generating accurate water quality models and detecting contamination peaks. The proposed method demonstrated improvements compared to the lawnmower approach. It achieved a remarkable 17% improvement, on r-squared data, in generating complete models of water quality parameters throughout the lake. In addition, it achieved a 230% improvement in accurate characterization of high pollution areas and a 24% increase in pollution peak detection specifically for heterogeneous fleets equipped with four or more identical sensors.

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“…Por una parte, el Modelado y la Simulación (M&S), basados en el uso y resolución de ecuaciones diferenciales (Carazo-Barbero et al, 2021, 2023 y/o Inteligencia Artificial (Jang et al, 2020), pueden intentar predecir, aunque con bastante incertidumbre, la localización y concentración del bloom. Por otra parte, los vehículos autónomos de superficie (ASVs, del inglés Autonomous Surface Vehicles), equipados con sondas multiparamétricas verticalmente desplazables, pueden facilitar la recogida de información relacionada con el bloom en numerosas localizaciones de la masa de agua (Girón-Sierra and Chacón-Sombría, 2021;Besada-Portas et al, 2021).…”

Section: Introductionunclassified

Tecnologías habilitadoras para automatizar la monitorización de blooms de cianobacterias

Esteban San Roman,

Girón-Sierra,

Pajares

et al. 2023

XLIV Jornadas De Automática: Libro De Actas: Universidad De Zaragoza, Escuela De Ingeniería Y Arquitectura, 6, 7 Y 8 De Septiem

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Los blooms de cianobacterias son procesos biológicos dinámicos, peligrosos para los habitantes y usuarios de los recursos hídricos, ya que reducen el oxigeno y el alcance de la luz de las masas del agua, y pueden producir toxinas altamente dañinas. La monitorización y la predicción automática de los blooms pueden permitir a las autoridades detectarlos y anticipar la toma de decisiones para mitigar los riesgos que éstos conllevan. El uso de tecnologías como el Modelado & Simulación, los Vehículos Autónomos de Superficie inteligentes, el Internet de las Cosas con computación en el borde, los Gemelos Digitales y la Inteligencia Artificial facilita tanto la monitorización como la predicción y la gestión basada en el conocimiento de la situación real de las masas de agua. Este artículo presenta diferentes avances y proyectos de investigación en estos campos del grupo de Ingeniería de Sistemas, Control, Automatización y Robótica de la Universidad Complutense de Madrid.

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EA-Based ASV Trajectory Planner for Pollution Detection in Lentic Waters

Cited by 5 publications

References 11 publications

A Bleeding Edge Web Application for Early Detection of Cyanobacterial Blooms

A Bleeding Edge Web Application for Early Detection of Cyanobacterial Blooms

AquaHet-PSO: An Informative Path Planner for a Fleet of Autonomous Surface Vehicles With Heterogeneous Sensing Capabilities Based on Multi-Objective PSO

Tecnologías habilitadoras para automatizar la monitorización de blooms de cianobacterias

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