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Real Time Water Quality Forecasting at Dalchun Using Nonlinear Stochastic Model  

Yeon, In-sung (충북대학교 토목공학과)
Cho, Yong-jin (충주대학교 환경공학과)
Kim, Geon-heung (인하대학교 환경토목공학부)
Publication Information
Journal of Korean Society of Water and Wastewater / v.19, no.6, 2005 , pp. 738-748 More about this Journal
Abstract
Considering pollution source is transferred by discharge, it is very important to analyze the correlation between discharge and water quality. And temperature also influent to the water quality. In this paper, it is used water quality data that was measured DO (Dissolved Oxygen), TOC (Total Organic Carbon), TN (Total Nitrogen), TP (Total Phosphorus) at Dalchun real time monitoring stations in Namhan river. These characteristics were analyzed with the water quality of rainy and nonrainy periods. Input data of the water quality forecasting models that they were constructed by neural network and neuro-fuzzy was chosen as the reasonable data, and water quality forecasting models were applied. LMNN (Levenberg-Marquardt Neural Network), MDNN (MoDular Neural Network), and ANFIS (Adaptive Neuro-Fuzzy Inference System) models have achieved the highest overall accuracy of TOC data. LMNN and MDNN model which are applied for DO, TN, TP forecasting shows better results than ANFIS. MDNN model shows the lowest estimation error when using daily time, which is qualitative data trained with quantitative data. If some data has periodical properties, it seems effective using qualitative data to forecast.
Keywords
artificial intelligence; neural network; neuro-fuzzy; real time; water quality forecast;
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