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REAL‐TIME PREDICTION OF CHLOROPHYLL‐ A TIME SERIES IN A EUTROPHIC AGRICULTURAL RESERVOIR IN A COASTAL ZONE USING RECURRENT NEURAL NETWORKS WITH PERIODIC CHAOS NEURONS
Author(s) -
Harada Masayoshi,
Tominaga Takafumi,
Hiramatsu Kazuaki,
Marui Atsushi
Publication year - 2013
Publication title -
irrigation and drainage
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.421
H-Index - 38
eISSN - 1531-0361
pISSN - 1531-0353
DOI - 10.1002/ird.1757
Subject(s) - eutrophication , artificial neural network , water quality , fractal dimension , time series , chaotic , phytoplankton , fractal , environmental science , biological system , series (stratigraphy) , hydrology (agriculture) , computer science , mathematics , artificial intelligence , ecology , statistics , geology , biology , nutrient , mathematical analysis , geotechnical engineering , paleontology
To assess the water environmental dynamics related to a phytoplankton, the water quality dynamics in a eutrophic reservoir in a flat low‐lying agricultural area were analyzed from the viewpoint of short‐time prediction of time series data using artificial intelligence. A recurrent neural network model with periodic chaos neurons was used for the real‐time prediction of chlorophyll‐ a time series on the basis of on‐site continuous observation data. These data consisted of the chlorophyll‐ a from four algae classes, Chlorophyceae, cyanobacteria, diatom/dinoflagellates, and cryptophytes, measured by a submerged fluorometer. The results suggest that study of a neural network could be performed sufficiently for teaching data of which a value of the fractal dimension calculated by the Higuchi method was smaller. In addition, it is possible to conduct a short‐time prediction of chlorophyll‐ a time series such that an upper limit of lead time could be beyond 12 h when there is an analogous time–frequency characteristic between the teaching and the predicting data. In conclusion, a recurrent chaotic neural network could be an effective analysis tool for short‐time prediction of water quality on the basis of continuous observations, and the potential for prediction can be determined quantitatively using Higuchi's fractal dimension and time–frequency maps. Copyright © 2013 John Wiley & Sons, Ltd.