Extraction of a Parameter as an Index to Assess Water Quality of the Tamagawa, Tokyo, Japan, by Using Neural Networks and Multivariate Analysis

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Parameters as an index to efficiently assess the pollution level of the upper, middle, and lower streams of the Tamagawa (Tokyo, Japan) based on measured water quality were determined by using multivariate analysis, principal component analysis (PCA), and cluster analysis (CA) for data measured from 1994 to 2002. Missing data during 2000-2002 were estimated using a perceptron type neural network and arithmetic progression. The combination of scores for the first and second principal components obtained by PCA enabled classification of the upper, middle, and lower streams of the Tamagawa. The CA results corresponded well with the PCA results. Based on the score of the first principal compornent determined here, contributions to the water pollution of the middle and lower streams should be decreased to improve the water quality of the Tamagawa.
日本コンピュータ化学会の論文

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