Application of Support Vector Machine in Assessment of Aquatic Ecological Environment
Date Issued
2005
Date
2005
Author(s)
Chen, Ying-Shou
DOI
zh-TW
Abstract
The main purpose of this study is to propose an aquatic ecological assessment model by the Support Vector Machine (SVM). A number of hydrological factors and water quality with investigated information of fishes are first collected and used as the base of assessment of river ecological condition. The type of collected fishes in a specific river section is then transferred into fish indexes to indicate the suitability of the investigating aquatic ecology. The SVM is then used to construct the aquatic assessment model based on the hydrological factors and water quality information.
The SVM is a linear machine rooted in the statistical learning theory to construct a hyperplane as the decision surface in such a way that the margin of separation between positive and negative examples is maximized. The SVM has been broadly used in solving many scientific problems, especially in pattern classification and nonlinear regression. For the purpose of comparison, three traditional classifying methods, i.e. K-means, fuzzy C-mean, and discrimination, were also used to construct the assessment model.
The measured hydrological data and investigated fishes data obtained in Chihlan creek and Fazih creek River were used to train and test the models. The results demonstrate that the SVM get much better performance than the three traditional classify methods. The cross validation results of accuracy in both creeks obtained by SVM are over 91%. The results suggest that the SVM is a powerful and suitable tool for building an aquatic ecological assessment model.
Subjects
支援向量機
魚類評估指標
水域生態
Support vector machine
fish indexes
aquatic ecological
Type
thesis
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