title: Analysis of algae growth mechanism and water bloom prediction under the effect of multi-affecting factor.
abstract: The formation process of algae is described inaccurately and water blooms are predicted with a low precision by current methods. In this paper, chemical mechanism of algae growth is analyzed, and a correlation analysis of chlorophyll-a and algal density is conducted by chemical measurement. Taking into account the influence of multi-factors on algae growth and water blooms, the comprehensive prediction method combined with multivariate time series and intelligent model is put forward in this paper. Firstly, through the process of photosynthesis, the main factors that affect the reproduction of the algae are analyzed. A compensation prediction method of multivariate time series analysis based on neural network and Support Vector Machine has been put forward which is combined with Kernel Principal Component Analysis to deal with dimension reduction of the influence factors of blooms. Then, Genetic Algorithm is applied to improve the generalization ability of the BP network and Least Squares Support Vector Machine. Experimental results show that this method could better compensate the prediction model of multivariate time series analysis which is an effective way to improve the description accuracy of algae growth and prediction precision of water blooms.
authors.affiliation: Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, 100048 Beijing, China.
authors.forename: Li
authors.initials: L
authors.lastname: Wang
authors.affiliation: Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, 100048 Beijing, China.
authors.forename: Xiaoyi
authors.initials: X
authors.lastname: Wang
authors.affiliation: Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, 100048 Beijing, China.
authors.forename: Xuebo
authors.initials: X
authors.lastname: Jin
authors.affiliation: Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, 100048 Beijing, China.
authors.forename: Jiping
authors.initials: J
authors.lastname: Xu
authors.affiliation: Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, 100048 Beijing, China.
authors.forename: Huiyan
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, 100048 Beijing, China.
authors.forename: Jiabin
authors.initials: J
authors.lastname: Yu
authors.affiliation: Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, 100048 Beijing, China.
authors.forename: Qian
authors.initials: Q
authors.lastname: Sun
authors.affiliation: Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, 100048 Beijing, China.
authors.forename: Chong
authors.initials: C
authors.lastname: Gao
authors.affiliation: Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, 100048 Beijing, China.
authors.forename: Lingbin
authors.initials: L
authors.lastname: Wang
keywords: Algae growth; Chemical mechanism; Multi-factor; Prediction; Water blooms