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Application of fuzzy regression analysis and fuzzy C‐means technique using UAV data to understand water quality in the Miharu Dam Reservoir, Japan
Author(s) -
Kageyama Yoichi,
Wakatabe Koki,
Ishikawa Masato,
Kobori Bunyuu,
Nagamoto Daisuke
Publication year - 2018
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22745
Subject(s) - fuzzy logic , regression analysis , water quality , regression , environmental science , fuzzy clustering , blue green algae , hydrology (agriculture) , computer science , data mining , statistics , mathematics , engineering , artificial intelligence , machine learning , geology , ecology , biology , geotechnical engineering , cyanobacteria , paleontology , bacteria
This letter reports on the water quality conditions of the Miharu Dam Reservoir estimated by both fuzzy regression analysis and fuzzy c‐means clustering. Growth of blue‐green algae in the summer significantly degrades the water quality of this lake. Near‐infrared (NIR) data was collected by an unmanned aerial vehicle (UAV) in August 2015, and these results were then compared with the results obtained from the simple fuzzy regression analysis. The use of both the fuzzy regression analysis and the fuzzy c‐means technique allowed a better understanding of the water surface conditions of the lake, especially in relation to blue‐green algae growth. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.