An Improved Hybrid ARIMA and Support Vector Machine Model for Water Quality Prediction

Yishuai Guo, Guoyin Wang, Xuerui Zhang, Weihui Deng

Abstract: Traditionally, the hybrid ARIMA and support vector machine model has been often used in time series forecasting. Due to the unique variability of water quality monitoring data, the hybrid model cannot easily give perfect forecasting. Therefore, this paper proposed an improved hybrid methodology that exploits the unique strength in predicting water quality time series problems. Real data sets of water quality provided by the Ministry of Environmental Protection of People’s Republic of China during 2008-2014 were used to examine the forecasting accuracy of proposed model. The results of computational tests are very promising.