A database application framework toward data-driven vertical connectivity analysis of rivers

Negreiros, Beatriz; Schwindt, Sebastian; Scolari, Federica; Barros, Ricardo; Galdos, Alcides Aybar; Noack, Markus; Haun, Stefan; Wieprecht, Silke

doi:10.1016/j.envsoft.2023.105916

Cited by 1 publication

(1 citation statement)

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“…For instance, machine learning models have proven effective for simulating HACBs in water systems [9]. It can be seen from previous works how obtaining knowledge from complex aquatic environment data sources is a challenge that requires extensive software engineering knowledge [10].…”

Section: Introduction and Related Workmentioning

confidence: 99%

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

Andrade,

Esteban,

Risco-Martín

et al. 2024

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In response to the challenges introduced by global warming and increased eutrophication, this paper presents an innovative modeling and simulation (M&S)-driven model for developing an automated inland water monitoring system. This system is grounded in a layered Internet of Things (IoT) architecture and seamlessly integrates cloud, fog, and edge computing to enable sophisticated, real-time environmental surveillance and prediction of harmful algal and cyanobacterial blooms (HACBs). Utilizing autonomous boats as mobile data collection units within the edge layer, the system efficiently tracks algae and cyanobacteria proliferation and relays critical data upward through the architecture. These data feed into advanced inference models within the cloud layer, which inform predictive algorithms in the fog layer, orchestrating subsequent data-gathering missions. This paper also details a complete development environment that facilitates the system lifecycle from concept to deployment. The modular design is powered by Discrete Event System Specification (DEVS) and offers unparalleled adaptability, allowing developers to simulate, validate, and deploy modules incrementally and cutting across traditional developmental phases.

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Section: Introduction and Related Workmentioning

confidence: 99%