Water bloom prediction and factor analysis based on multidimensional time series analysis

WANG Li; LIU Zaiwen; WU Chengrui; HUA Wei; ZHANG Xue

doi:10.3969/j.issn.0438-1157.2013.12.056

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Water bloom prediction and factor analysis based on multidimensional time series analysis

更新时间：2025-12-10

- Water bloom prediction and factor analysis based on multidimensional time series analysis
- CIESC Journal Issue 12, Pages: 4649-4655(2013)
- 作者机构：
  
  1. 北京工商大学计算机与信息工程学院,北京,100048
  2. 北京安恒测试技术有限公司,北京,100048
  3. 苏州市自来水公司,江苏,苏州,215012
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0438-1157.2013.12.056
  CLC： Q019
- Online First：05 December 2013，
  
  Published：05 December 2013
- 稿件说明：
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WANG Li, LIU Zaiwen, WU Chengrui, et al. Water bloom prediction and factor analysis based on multidimensional time series analysis[J]. CIESC Journal, 2013, (12): 4649-4655.
DOI：

WANG Li, LIU Zaiwen, WU Chengrui, et al. Water bloom prediction and factor analysis based on multidimensional time series analysis[J]. CIESC Journal, 2013, (12): 4649-4655. DOI： 10.3969/j.issn.0438-1157.2013.12.056.

摘要

在水华防治工作中，水华预测一直都是一个难点，为了解决由于水华生成过程中多种特征因素间的交互影响建模困难，现有的水华预测方法预测结果还不够准确，以及不同影响因素与水华发生的相关性程度的判定等问题，采用多元时间序列分析技术，研究多特征因素的水华预测及因素分析方法，通过对水华生成过程中的特征因素时序建模分析，提出多重潜周期多元自回归模型，给出了基于多元周期平稳时序分析的水华预测以及因素分析结果。采用本文方法及传统方法分别对江苏太湖水华特征因素监测数据进行建模预测，结果表明，基于本文方法的水华特征因素预测结果与实测结果更相符、预测平均误差绝对值更小。

Abstract

In water bloom prevention and control

water bloom prediction is always a difficult problem. This paper proposes a new water bloom prediction method based on multiple characteristic factors time series analysis to take into account the integrated effect of multiple characteristic factors along with the periodicity and random effect of environmental variables

to solve the problem that existing bloom prediction is not accurate enough

and to analyze the correlation between influential factors and water bloom.A multidimensional hidden periodic-auto regression (MHPAR) model is put forward based on the characteristic factors time series.A water bloom prediction method and an influential factors analysis method are put forward by using multidimensional period stationary time series analysis.Comparing the proposed model with other traditional time series models

such as auto regression (AR) model

hidden periodic-auto regression (HPAR) model and multidimensional auto regression (MAR) model

it has been found that multidimensional hidden periodic-auto regression model is useful and accurate for establishing multiple characteristic factors time series of water bloom.

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