Convolutional Neural Network Considering Physical Processes and its Application to Diatom Detection

Convolutional Neural Network (CNN) considering physical processes with time series of stages for diatom detection with remote sensing satellite derived physical data (Chlorophyll-a, Photosynthesis Available Radiance (PAR), Turbidity, Sea Surface Temperature (SST)) and meteorological data is proposed. Diatom is bloomed under the condition of suitable sea water temperature, nutrition rich water (Chlorophyll-a derived from river water flow), photosynthesis available radiance derived from solar irradiance, transparency of the sea water for photosynthesis (turbidity), and sea water convection between bottom sea water and sea surface water. Almost all the conditions can be monitored by remote sensing satellite-based radiometers. The proposed diatom prediction based on convolutional neural network with remote sensing satellite and meteorological data is validated. Through the experiments at Ariake bay area, Kyushu, Japan with gathered time series of remote sensing data of Moderate resolution of Imaging Spectroradiometer (MODIS) derived turbidity as well as chlorophyll-a data estimated for the winter seasons (from January to March) during from 2010 to 2018 together with measured and acquired meteorological data for the same winter seasons, the proposed method is validated.

@article{Arai2019,
  title     = {Convolutional Neural Network Considering Physical Processes and its Application to Diatom Detection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {10},
  number    = {12},
  year      = {2019},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai},
  doi       = {10.14569/IJACSA.2019.0101214},
  url       = {https://doi.org/10.14569/IJACSA.2019.0101214}
}