|Table of Contents|

Taihu Lake Eutrophication Index Analysis and PredictionBased on Multiple Regression and Neural Network(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2017年02期
Page:
70-
Research Field:
计算机工程
Publishing date:

Info

Title:
Taihu Lake Eutrophication Index Analysis and PredictionBased on Multiple Regression and Neural Network
Author(s):
Wang Kaixiang
School of Computer Science and Technology,Nanjing Normal University,Nanjing 210023,China
Keywords:
eutrophication; forecast; correlation coefficient; the multivariate linear regression analysis; the BP neural network
PACS:
TP18
DOI:
10.3969/j.issn.1672-1292.2017.02.011
Abstract:
Eutrophication of body of water is a major environmental problem of the Taihu lake,and the eutrophication prediction is an effective early warning method to know the change of lake water quality. Now there are a lot of prediction schemes depending on the existing known factors related to the target,and it is difficult to fully accurate analysis of the relationship between the related factors and predict the change trend of target variable. In this paper,firstly,we find out all of the possible affecting factors of the eutrophication of Taihu lake,then we can obtain a better target variable of related factors with a more comprehensive variables provided. We select the eutrophication of total nitrogen as the object of analysis and forecasting. The factors are selected by their correlation with total nitrogen. We conduct the prediction study for the change of total nitrogen with multiple linear regression analysis method and BP neural network prediction method,and compare the performances of the two methods. The results show that the variables selected can well predict the changes of total nitrogen. The experimental results obtained from multiple linear regression and BP neural network method are both accurate,from the perspectives of the goodness of fit and the mean square error,and the BP neural network is better.

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Last Update: 2017-06-30