Research of predictive model of drinking water quality based on wavelet neural network

Deng Yu; et al

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Research of predictive model of drinking water quality based on wavelet neural network

更新时间：2024-01-17

- Research of predictive model of drinking water quality based on wavelet neural network
- Journal of Chongqing Medical University Vol. 34, Issue 4, Pages: 455-458(2009)
- 作者机构：
  
  重庆医科大学公共卫生学院卫生统计教研室
- 作者简介：
- 基金信息：
- DOI：
  CLC： TU991.21
- Published：2009，
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[1]邓宇,姚宁,田考聪.基于小波神经网络的饮用水水质预测模型研究[J].重庆医科大学学报,2009,34(04):455-458.

Deng Yu, et al. Research of predictive model of drinking water quality based on wavelet neural network. [J]. Journal of Chongqing Medical University 34(4):455-458(2009)
[1]邓宇,姚宁,田考聪.基于小波神经网络的饮用水水质预测模型研究[J].重庆医科大学学报,2009,34(04):455-458. DOI：

Deng Yu, et al. Research of predictive model of drinking water quality based on wavelet neural network. [J]. Journal of Chongqing Medical University 34(4):455-458(2009) DOI：

摘要

目的:为饮用水水质预测提供方法学参考。方法:采用小波神经网络（Wavelet neural network

WNN）方法建立水质预测模型

对重庆市某主城区饮用水水质参数高锰酸钾月平均浓度进行预测

并同BP神经网络的预测结果进行比较分析。结果:采用均方根误差（Root mean square error

RMSE）和平均绝对百分比误差（Mean absolute percentage error

MAPE）来评价模型预测效果。预测实例表明WNN模型具有较高的预测精度

并且预测精度高于BP神经网络。结论:利用WNN对水质参数具有较好的预测效果。

Abstract

Objective:To provide the methodology reference for the drinking water quality prediction.Methods:A predictive model of drinking water quality was established by wavelet neural network.The monthly average concentration of potassium permanganate in Chongqing

one drinking water quality parameter

was predicted by the model

and the predictive results were compared with BP neural network.Results:RMSE and MAPE were applied to evaluate the predictive results.The research indicated that the precision of WNN model was superior to that of BP neural network model.Conclusion:The WNN model has better precision for drinking water quality prediction.

关键词

小波神经网络小波分析BP神经网络水质预测

Keywords

Wavelet neural networkWavelet analysisBP neural networkDrinkingwater quality prediction

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