Water Quality Prediction Based on Exponential Smoothing-Gray Model for Kuncheng Lake - RONG Jie; ZHANG Fengtai (Renmin Zhujiang)
Predicting the development trend of surface water environmental quality is an important basis for managing the current water environment as it provides an important reference for water environment planning,evaluation,and management.The water quality data total phosphorus of the central section of Kuncheng Lake for 2012—2021 are leveraged to build an exponential smoothing-grey prediction GM (1, 1) model.The results show that after smoothing,the model can filter out some short-term irregular changes in the data,that is,small-scale fluctuations in water quality,to reduce the randomness of water quality data so that the data can better reflect the overall trend.The model output value (simulated value) fits well with the original sequence (smoothed value).After the model passes the test,the total phosphorus that restricts the section from reaching the class Ⅲ water standard is predicted.The results show that room for further improvement remains for the central section of the lake to reach the class Ⅲ lake water standard during the 14th Five-Year Plan period.Close attention should continue to be paid to improving the water quality of the section to reach the standard,and sustained efforts should be made to strengthen the treatment and ecological restoration of the lake.