Nonlinear dynamics analysis and water bloom prediction of cyanobacteria growth time variation system

WANG Li; GAO Chong; WANG Xiaoyi; LIU Zaiwen

doi:10.11949/j.issn.0438-1157.20161622

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Nonlinear dynamics analysis and water bloom prediction of cyanobacteria growth time variation system

更新时间：2025-12-10

- Nonlinear dynamics analysis and water bloom prediction of cyanobacteria growth time variation system
- CIESC Journal Issue 3, Pages: 1065-1072(2017)
- 作者机构：
  
  北京工商大学计算机与信息工程学院,北京,100048
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20161622
  CLC： Q019
- Online First：05 March 2017，
  
  Published：05 March 2017
- 稿件说明：
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WANG Li, GAO Chong, WANG Xiaoyi, et al. Nonlinear dynamics analysis and water bloom prediction of cyanobacteria growth time variation system[J]. CIESC Journal, 2017, (3): 1065-1072.
DOI：

WANG Li, GAO Chong, WANG Xiaoyi, et al. Nonlinear dynamics analysis and water bloom prediction of cyanobacteria growth time variation system[J]. CIESC Journal, 2017, (3): 1065-1072. DOI： 10.11949/j.issn.0438-1157.20161622.

摘要

为解决现有蓝藻生长动力学模型难以有效描述实际水体中蓝藻生长时变系统的非线性动力学特性，导致水华预测准确性不高的问题，构建蓝藻摄食和营养盐循环模型，并考虑水温、光照等主要影响因素随时间变化对蓝藻生长的影响，进一步建立蓝藻生长时变系统非线性动力学模型，对其常值参数采用遗传算法与数值算法结合的方法进行优化率定，对其时变参数采用多元时序方法进行建模预测，根据分岔理论及时变系统理论分析水华暴发行为的非线性动力学机理，实现对蓝藻生长时变系统的水华预测。通过太湖流域监测实例表明，与现有研究相比，引入时变参数的蓝藻生长动力学模型更能反映蓝藻生长时变系统下水华暴发行为的非线性动力学特性，其水华预测结果更为准确。

Abstract

In order to solve the problem that the actual description of the water bloom behavior is not entirely conform to reality and water bloom prediction is not accurate enough by existing algae growth dynamics model due to the neglect of model parameters change with time

this paper builds cyanobacteria feeding and nutrient cycling model

and proposes algae growth dynamics model with time-varying parameters based on time variation influences of water temperature and illumination on model parameters. The calibration method for constant parameters of the model is optimized based on genetic and numerical algorithm. And the model time-varying parameters is modeled and predicted by multivariate time series method. Nonlinear dynamic mechanism of cyanobacteria bloom behavior is analyzed by bifurcation theory and time varying system theory

and then a new method of cyanobacteria bloom prediction is put forward. The monitoring data analysis of the Taihu River Basin in Jiangsu shows that cyanobacterial growth dynamics model with time-varying parameters can reflect nonlinear dynamic characteristics of cyanobacteria bloom behavior in cyanobacteria growth time-varying system. The model is more consistent with the actual situation

and cyanobacteria bloom prediction result is more accurate.

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