ECOMSNet – An edge computing-based sensory network for real-time water level prediction and correction

Tsun Hua Yang*, Chia Wei Wang, Sheng Jhe Lin

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Water level monitoring and forecasting are essential tasks in flood emergency response. This study proposes an Edge COMputing-based Sensory NETwork (ECOMSNet), an innovative decentralized early warning system (EWS), for water level monitoring and prediction. A sensor-embedded algorithm integrates the direct step method (DSM) with a microgenetic algorithm (MGA). This algorithm predicts the water surface profile and corrects it once water level observations are available. It also meets efficiency requirements to accommodate sensor computation limitations. The errors in the predicted water surface profiles in channels with gradually varied flows are 5% in a laboratory flume experiment and below 10% in a field experiment. The ECOMSNet is an achievement of edge computing-based Internet of Things. It shows potential to increase emergency response efficiency. However, the system requires further refinement and testing if it is to adequately address rapidly varied unsteady flow in a scaled-up implementation.

Original languageEnglish
Article number104771
JournalEnvironmental Modelling and Software
Volume131
DOIs
StatePublished - Sep 2020

Keywords

  • Early warning system
  • Edge computing
  • IoT
  • Microgenetic algorithm
  • Water level prediction

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