Image Sensors for Wave Monitoring in Shore Protection: Characterization through a Machine Learning Algorithm.

Journal: Sensors (Basel, Switzerland)
Published Date:

Abstract

Waves propagating on the water surface can be considered as propagating in a dispersive medium, where gravity and surface tension at the air-water interface act as restoring forces. The velocity at which energy is transported in water waves is defined by the group velocity. The paper reports the use of video-camera observations to study the impact of water waves on an urban shore. The video-monitoring system consists of two separate cameras equipped with progressive RGB CMOS sensors that allow 1080p HDTV video recording. The sensing system delivers video signals that are processed by a machine learning technique. The scope of the research is to identify features of water waves that cannot be normally observed. First, a conventional modelling was performed using data delivered by image sensors together with additional data such as temperature, and wind speed, measured with dedicated sensors. Stealth waves are detected, as are the inverting phenomena encompassed in waves. This latter phenomenon can be detected only through machine learning. This double approach allows us to prevent extreme events that can take place in offshore and onshore areas.

Authors

  • Aimé Lay-Ekuakille
    Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy.
  • John Peter Djungha Okitadiowo
    Department of Computer Science, Technological University Bel Campus, 03 Kinshasa, Congo.
  • Diana Di Luccio
    Science and Technologies Department, University of Naples "Parthenope", 80133 Naples, Italy.
  • Maurizio Palmisano
    CNR, National Research Council, Experimental Research Center, 82100 Benevento, Italy.
  • Giorgio Budillon
    Science and Technologies Department, University of Naples "Parthenope", 80133 Naples, Italy.
  • Guido Benassai
    Engineering Department, University of Naples "Parthenope", 80133 Naples, Italy.
  • Sabino Maggi
    CNR, National Research Council, Institute of Atmospheric Pollution Research, 70126 Bari, Italy.