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Application of Regression Analytical Method in Dynamic Prediction of River Water Quality


Affiliations
1 North China University of Water Resources and Electric Power, Zhengzhou 450045, China
2 Shandong Survey and Design Institute of Water Conservancy, Shandong 250013, China
 

It is very important to accurately predict the river water quality. Prediction of river water quality has been closely watched in water resources evaluation, and is the primary work of scientific planning and management of water resources and exploitation. The accuracy of prediction will directly influence whether we can work out a reasonable plan and management measures. According to the relationship of river water quality with the influencing factors, regression method is used to predict the tendency of river water quality. The influencing factors include rainfall, sediment and runoff. This study could provide reference and guidance for further exploitation of river water.

Keywords

Regression Model, River Water Quality, Prediction.
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  • Application of Regression Analytical Method in Dynamic Prediction of River Water Quality

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Authors

Jianbo Pan
North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Gao Zhou
North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Dedong Liu
Shandong Survey and Design Institute of Water Conservancy, Shandong 250013, China

Abstract


It is very important to accurately predict the river water quality. Prediction of river water quality has been closely watched in water resources evaluation, and is the primary work of scientific planning and management of water resources and exploitation. The accuracy of prediction will directly influence whether we can work out a reasonable plan and management measures. According to the relationship of river water quality with the influencing factors, regression method is used to predict the tendency of river water quality. The influencing factors include rainfall, sediment and runoff. This study could provide reference and guidance for further exploitation of river water.

Keywords


Regression Model, River Water Quality, Prediction.