[1]QIAN Yun,LIANG Yanchun,ZHAI Tianfang,et al.Evolutionary support vector machine model and its application in water quality assessment[J].CAAI Transactions on Intelligent Systems,2015,10(5):684-689.[doi:10.11992/tis.201410018]
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Evolutionary support vector machine model and its application in water quality assessment

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 5 Page number: 684-689 Column: 学术论文—机器学习 Public date: 2015-10-25
Title:
Evolutionary support vector machine model and its application in water quality assessment
Author(s):
QIAN Yun1; 2;  LIANG Yanchun1;  ZHAI Tianfang3;  LIU Hongzhi4;  SHI Xiaohu1
1. College of Computer Science and Technology, Jilin University, Changchun 130012, China;
2. College of Electrical and Information Engineering, Beihua University, Jilin 132021, China;
3. Jilin Water Resources Research Institute, Changchun 130022, China;
4. Computing Center of Jilin Province, Computer Technology Research Institute of Jilin Province, Changchun 130012, China
Keywords:
water quality assessment model; support vector machine (SVM); genetic algorithms (GA); radial basis kernel function; penalty factor
CLC:
TP391.4
DOI:
10.11992/tis.201410018
Abstract:
A water quality assessment model is an effective tool for water quality planning, environmental water pollution control and environment management. In this paper, an evolutionary support vector machine (SVM) model is developed by using genetic algorithm (GA) to combine and optimize the radial basis kernel function parameter σ and error penalty factor C of a SVM algorithm. This model is then extended to water quality assessment. To test the effectiveness of the proposed method, it is applied to a simulation on real data of the Songyuan and Harbin sections of the Songhua River, the Gansu section of the Yellow River, and the Jilin Huadian Guanmenlizi water reservoir. Simulation results show that, compared with the classical SVM method, the classification accuracy and generalization ability of the evolutionary support vector machine model for water quality assessment are improved.
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