Forecasting Water Quality Parameters by ANN Model using Pre-processing Technique at The Downstream of Cheongpyeong Dam

被引：28

作者：

Seo, Il Won ^{[1
]}

Yun, Se Hun ^{[1
]}

Choi, Soo Yeon ^{[1
]}

机构：

[1] Seoul Natl Univ, 1 Gwanak Ro, Seoul 08826, South Korea

来源：

12TH INTERNATIONAL CONFERENCE ON HYDROINFORMATICS (HIC 2016) - SMART WATER FOR THE FUTURE | 2016年 / 154卷

关键词：

Artificial Neural Network; Ensemble modeling; stratified sampling; water quality forecasting; North Han River;

D O I：

10.1016/j.proeng.2016.07.519

中图分类号：

TV21 [水资源调查与水利规划];

学科分类号：

081501 ;

摘要：

In this study, the water quality parameters (Temperature, DO, pH, Electric Conductivity, TN, TP, Turbidity and Chlorophyll-a) at the downstream of Cheongpyeong dam are predicted using artificial neural network. The artificial neural network(ANN) is a powerful computational technique for modeling complex relationship between input and output data. Typically, Time series generally consists of a linear combination of trend, periodicity and stochastic component. In this study, to reduce the influence of trend, periodicity and stochastic component and to enhance the performance of ANN model, developed the Ensemble ANN model with stratified sampling method. 7 parameters (Temperature, DO, pH, Electric Conductivity, TN, TP, and Chlorophyll-a) have the higher than 0.85 R squared. And 5 parameters (Temperature, DO, pH, TN, and TP shows than 1.0 RMSE

引用

页码：1110 / 1115

页数：6