Study on Integrated Prediction Model of Coastal Water Quality Based on Log-logistic Probability Distribution
基于log2logistic 概率分布的近海水质组合预测方法研究

Through the using of different prediction methods,a new integrated prediction model for coastal water quality based on monitoring data was proposed,which aimed to reducing calculation difficulty and prediction errors.Firstly,the cause-effect prediction was taken using BP NN(back-propagation neural network),whose inputs were data about the incoming water.The average prediction error of this method was 26.46%.Secondly,the monitoring data serial of each monitoring point was fitted by Fourier serial,after which the fitted Fourier serial was also used for prediction and the error was 38.33%.Finally,the integrated prediction was taken based on the above two prediction results,whose weights were calculated by its log-logistic probability density and average prediction error was reduced to 21.20%.Through application it could be found that the extreme demand on basic data in mechanism studies could be avoided,which made the method in this paper simple,practicable and could be the decision support for environmental management.