基于K-means聚类和ELM神经网络的养殖水质溶解氧预测

为解决养殖水质溶解氧预测传统方法引入不良样本、精度低等问题,该文以2014、2015年江苏常州养殖基地水质和气象数据为基础,提出了一种基于K-means聚类和ELM神经网络（extreme learning machine,ELM）的溶解氧预测模型。采用皮尔森相关系数法确定环境因素与溶解氧的相关系数,自定义相似日的统计量-相似度,通过K-means聚类方法将历史日样本划分为若干类,然后分类识别获得与预测日最相似的一类历史日样本集,将其与预测日的实测环境因素作为预测模型的输入样本建立ELM神经网络溶解氧预测模型。试验结果表明,该模型均具有较快的计算速度和较高的预测精度,在常规天气下,平均绝对百分...

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Bibliographic Details
Published in农业工程学报 Vol. 32; no. 17; pp. 174 - 181
Main Author 宦娟 刘星桥
Format Journal Article
LanguageChinese
Published 常州大学信息科学与工程学院，常州 213164%江苏大学电气信息工程学院,镇江,212013 2016
江苏大学电气信息工程学院，镇江 212013
Subjects
ELM神经网络
K-means聚类
养殖
模型
溶解氧预测
相似日
神经网络
models
模型
neural networks
相似日
神经网络
ELM neural network
K-means clustering
ELM神经网络
similar day
养殖
K-means聚类
aquaculture
dissolved oxygen prediction
溶解氧预测
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ISSN1002-6819
DOI10.11975/j.issn.1002-6819.2016.17.024

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Summary:为解决养殖水质溶解氧预测传统方法引入不良样本、精度低等问题,该文以2014、2015年江苏常州养殖基地水质和气象数据为基础,提出了一种基于K-means聚类和ELM神经网络（extreme learning machine,ELM）的溶解氧预测模型。采用皮尔森相关系数法确定环境因素与溶解氧的相关系数,自定义相似日的统计量-相似度,通过K-means聚类方法将历史日样本划分为若干类,然后分类识别获得与预测日最相似的一类历史日样本集,将其与预测日的实测环境因素作为预测模型的输入样本建立ELM神经网络溶解氧预测模型。试验结果表明,该模型均具有较快的计算速度和较高的预测精度,在常规天气下,平均绝对百分误差和均方根误差分别达到1.4%、10.8%;在突变天气下,平均绝对百分误差和均方根误差分别达到2.6%和11.6%,有利于水产养殖水质精准调控。
Bibliography:Dissolved oxygen plays a vital role in water management as it is an important factor that determines the growth status of the fish. Either inadequate or excessive level of dissolved oxygen will be harmful to the survivability of the fish in their respective habitats. The accurate analysis of the data collected from the aquaculture ponds and the prediction for the anticipated level of dissolved oxygen are helpful for both water quality management and aquaculture production. Current studies reveal and understand the complex features of the water quality process mainly from the perspective of mathematical statistics. However, they cannot analyze the effects of changes in the environment on water quality, and cannot do well in dissolved oxygen prediction under the changing environment either. This paper proposed a new strategy to predict dissolved oxygen based on K-means clustering and ELM（extreme learning machine） neural networks. As the curves of similar days showed high correlation of dissolved oxygen, the his
ISSN:1002-6819
DOI:10.11975/j.issn.1002-6819.2016.17.024