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一种ARIMA和MLP的混合模型,与Grasshopper优化算法一起,用于对水质的时间序列预测

Jie Su^1,2, Ziyu Lin^3,4, Fengwei Xu^3,4

¹Basin Research Center for Water Pollution Control, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China. sujie198607@163.com.

Scientific reports

|October 13, 2024

概括

一种结合自回归集成移动平均线 (ARIMA) 和多层感知器 (MLP) 的新型混合模型准确地预测了河水质量. 与传统方法相比,这种先进的方法可以为溶解氧气,水温和提供更好的预测.

科学领域:

环境科学环境科学
数据科学数据科学数据科学
水文学的水文学

背景情况:

有效的河流流域管理需要强有力的水质监测,以控制污染物水平.
传统的时间序列模型,如自回归集成移动平均 (ARIMA) 和多层感知器 (MLP),在捕捉复杂的水质动态方面存在局限性.
现有的模型不平等地解决水质数据中的线性和非线性模式.

研究的目的:

开发和评估用于预测河水质量组件的混合模型.
将混合模型的预测精度与独立的ARIMA和MLP模型进行比较.
通过整合线性和非线性建模能力来提高水质预测.

主要方法:

使用了结合ARIMA和MLP的混合模型,并通过Grasshopper优化算法进行了优化.
应用模型来预测108个月 (2011-2019) 期间溶解氧气,水温和的时间序列数据.
使用相关系数和准确度指数评估模型性能.

主要成果:

混合模型显示了高的相关系数:溶解氧为0.9,水温为0.91,为0.91.
与单个ARIMA和MLP模型相比,混合模型表现出更高的精度.
混合方法有效地捕捉了复杂的水质时间序列的非线性特征.

结论:

拟议的混合ARIMA-MLP模型提供了更可靠的水质预测.
这种综合方法在河流水质管理的传统建模技术上提供了显著的改进.
这些发现支持使用先进的混合模型进行有效的环境监测和决策.

关键词:

在阿里马,阿里马就是阿里马.预测预测预测预测草优化算法的优化算法在MLP中,MLP是MLP.水的质量水的质量

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