Prediction Analysis of Artificial Landscape Water Eutrophication
Authors
Jiaoyan Ai, Haiyang Xu, Yajuan Cai, Sizhi Wu, Zengqiang Lei
Corresponding Author
Jiaoyan Ai
Available Online March 2016.
- 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/).
Cite this article
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 -