Improved Grey Model Base on Exponential Smoothing for River Water Pollution Prediction
XIE Zheng-wen1,2 SU Kai-yu 1. Safety and Environment Institute, China Jiliang University Information center, China Jiliang University Hangzhou, China Hangzhou, China 2. School of Resources and Safety Engineering,Central South University Changsha, China
Abstract-The aim of this project is to develop a river water pollution forecasting model to forecast the major pollutant of pollution predictor. We present an improved Grey-based water quality of Yangtze River in Nanjing extension. prediction algorithm to forecast the trend of the river water pollution. We adopted grey prediction as a forecasting means II. General exponential smoothing model because of its fast calculation with as few as four data inputs Exponential smoothing is usually based on the premise needed. However, our preliminary study shows that the general that the level of time series should fluctuate about a constant Grey model, GM (1, 1) is inadequate to handle a volatile system. [8-9] The general GM (1, 1) prediction generates the dilemmas of level or change slowly over time . Under such a premise, dissipation and overshoots. In this study, the prediction is the water pollution time series yt()can be described by improved significantly by applying the exponential smoothing yt()=+β() t
