Water Quality Parameter Prediction Based On Artificial Neural Network And Grey Theory

Rapid economic development has brought enormous pressure on water environment, predicting changes of water quality exactly is critical and basal to protect the safety of water environment. This thesis studies on the prediction modeling method of water quality parameters based on normal time-series data in the background of the Three Gorges Reservoir.As the Three Gorges Reservoir started water storage for a short time, monitoring parameters are simple, monitoring frequency is low, the time series data of water quality parameters are characterized by small samples and sudden change of some sections in Three Gorges, traditional statistical forecasting methods are not fit for the application. According to the gray system theory for the"poor information"problem, BP neural network with strong nonlinear fitting capability, and Least Squares Support Vector Machines (LS-SVM) specifically for small samples of forecasting, the following main research findings and conclusions, which studied on the time series data of water quality parameters of Three Gorges Reservoir, are brought forward:â‘ As the computational complexity of traditional time series embedding dimension of phase space reconstruction, using related entropy to determine model order of residual sequence for reference, the improved related entropy algorithm was brought forward to determine Phase Space Reconstruction embedding dimension, and the monthly average concentration of water quality parameters from 2005 to 2008 are used as time series data. Comparison between the predicting results of BP neural network and LS-SVM model using simulated annealing (SA) for optimization parameters indicates the feasibility of the improved related entropy algorithm, BP shows obvious advantages of nonlinear fitting for time series data of larger scope, and LS-SVM shows better performance in short time predicting for time series data of smaller fluctuation range.â‘¡According to the characteristics of small samples and sudden change of water quality parameters in flood season from 1997 to 2008, combined with grey predicting model for"poor information"is characterized by low forecast accuracy but calculation speed, BP neural network with strong nonlinear fitting capability but requiring large number of samples, and the advantage of LS-SVM for small samples, the gray metabolism BP neural network prediction model and EL-SVM prediction model were proposed. The outputs of gray metabolism predicting model sets are used for the inputs of BP neural network, which solves the huge learning samples on nonlinear fitting. The pretreatment model is put forward to improve the smoothness of time series data for the inputs of LS-SVM, which uses SA for optimization parameters. Simulation results show that the grey metabolism BP prediction model improves the prediction accuracy significantly compared with BP and grey metabolism model; ELS-SVM is better than LS-SVM, shows the considerable prediction accuracy of grey metabolism BP model, and renews the prediction methods of time series data with small sudden change samples.According to the characteristics of time series data of water quality parameters in Three Gorges Reservoir, the forecasting models are established, which not only objectively reflect the trends of water quality, and provide the basis for the scientific decision-making, but also widen the theoretical research and applications of time series prediction technology.