Prediction Analysis of Artificial Landscape Water Eutrophication

DOI

10.2991/icesame-16.2016.230How to use a DOI?

Keywords

Chl-a; Eutrophication; BP; Multiple Regression; Genetic Algorithms; SVM.

Abstract

The chlorophyll a (Chl-a) can express algae biomass, it is one indicator of the degree of eutrophication. In this paper, we take the Mirror Lake locate in Guangxi University as research object, and we fit the relationship between Ch1-a with every ecological factors in water by multivariate regression models, BP neural network, BP based on Genetic Algorithms and Support Vector Machines(SVM) to evaluate the eutrophication. Using BP neural network prediction model for the evaluation of eutrophication has good results which provide a theoretical basis for the landscape eutrophication's prevention and cure.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jiaoyan Ai
AU  - Haiyang Xu
AU  - Yajuan Cai
AU  - Sizhi Wu
AU  - Zengqiang Lei
PY  - 2016/03
DA  - 2016/03
TI  - Prediction Analysis of Artificial Landscape Water Eutrophication
BT  - Proceedings of the 2016 International Conference on Education, Sports, Arts and Management Engineering
PB  - Atlantis Press
SP  - 1065
EP  - 1069
SN  - 2352-5398
UR  - https://doi.org/10.2991/icesame-16.2016.230
DO  - 10.2991/icesame-16.2016.230
ID  - Ai2016/03
ER  -