TY - JOUR
T1 - 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
AU - Harada, Masayoshi
AU - Tominaga, Takafumi
AU - Hiramatsu, Kazuaki
AU - Marui, Atsushi
PY - 2013/10
Y1 - 2013/10
N2 - 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 12h 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.
AB - 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 12h 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.
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U2 - 10.1002/ird.1757
DO - 10.1002/ird.1757
M3 - Article
AN - SCOPUS:84885141442
VL - 62
SP - 36
EP - 43
JO - Irrigation and Drainage
JF - Irrigation and Drainage
SN - 1531-0353
IS - S1
ER -