Application of Bayesian model and discriminant function analysis to the estimation of sediment source contributions  

作　　者：Pengfei Du Donghao Huang Duihu Ning Yuehong Chen Bing Liu Jian Wang Jingjing Xu 

机构地区：[1]State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin,China Institute of Water Resources and Hydropower Research,Beijing,100048,China [2]Faculty of Geographical Science,Beijing Normal University,Beijing,100875,China

出　　处：《International Journal of Sediment Research》2019年第6期577-590,共14页国际泥沙研究（英文版）

基　　金：supported by the National Key Research and Development Program [grant number 2016YFC0500802];the National Natural Science Foundation of China [grant number 41501299,41701322];the special fund of State Key Laboratory of Simulation and Regulation of a Water Cycle in a River Basin,China Institute of Water Resources and Hydropower Research [grant number SKL2018TS08];IWHR Research & Development Support Program [grant number SC0145B172019]

摘　　要：Bayesian and discriminant function analysis(DFA)models have recently been used as tools to estimate sediment source contributions.Unlike existing multivariate mixing models,the accuracy of these two models remains unclear.In the current study,four well-distinguished source samples were used to create artificial mixtures to test the performance of Bayesian and DFA models.These models were tested against the Walling-Collins model,a credible model used in estimation of sediment source contributions estimation,as a reference.The artificial mixtures were divided into five groups,with each group consisting of five samples with known source percentages.The relative contributions of the sediment sources to the individual and grouped samples were calculated using each of the models.The mean absolute error(MAE)and standard error of(SE)MAE were used to test the accuracy of each model and the robustness of the optimized solutions.For the individual sediment samples,the calculated source contributions obtained with the Bayesian(MAE?7.4%,SE?0.6%)and Walling-Collins(MAE?7.5%,SE?0.7%)models produced results which were closest to the actual percentages of the source contributions to the sediment mixtures.The DFA model produced the worst estimates(MAE?18.4%,SE?1.4%).For the grouped sediment samples,the Walling-Collins model(MAE?5.4%)was the best predictor,closely followed by the Bayesian model(MAE?5.9%).The results obtained with the DFA model were similar to the values for the individual sediment samples,with the accuracy of the source contribution value being the poorest obtained with any of the models(MAE?18.5%).An increase in sample size improved the accuracies of the Walling-Collins and Bayesian models,but the DFA model produced similarly inaccurate results for both the individual and grouped sediment samples.Generally,the accuracy of the Walling-Collins and Bayesian models was similar(p>0.01),while there were significant differences(p<0.01)between the DFA model and the other models.This study demonstrated that the Bayesian moBayesian and discriminant function analysis(DFA) models have recently been used as tools to estimate sediment source contributions.Unlike existing multivariate mixing models,the accuracy of these two models remains unclear.In the current study,four well-distinguished source samples were used to create artificial mixtures to test the performance of Bayesian and DFA models.These models were tested against the Walling-Collins model,a credible model used in estimation of sediment source contributions estimation,as a reference.The artificial mixtures were divided into five groups,with each group consisting of five samples with known source percentages.The relative contributions of the sediment sources to the individual and grouped samples were calculated using each of the models.The mean absolute error(MAE) and standard error of(SE) MAE were used to test the accuracy of each model and the robustness of the optimized solutions.For the individual sediment samples,the calculated source contributions obtained with the Bayesian(MAE=7.4%,SE=0.6%) and Walling-Collins(MAE=7.5%,SE=0.7%) models produced results which were closest to the actual percentages of the source contributions to the sediment mixtures.The DFA model produced the worst estimates(MAE=18.4%,SE=1.4%).For the grouped sediment samples,the Walling-Collins model(MAE=5.4%) was the best predictor,closely followed by the Bayesian model(MAE=5.9%).The results obtained with the DFA model were similar to the values for the individual sediment samples,with the accuracy of the source contribution value being the poorest obtained with any of the models(MAE=18.5%).An increase in sample size improved the accuracies of the Walling-Collins and Bayesian models,but the DFA model produced similarly inaccurate results for both the individual and grouped sediment samples.Generally,the accuracy of the Walling-Collins and Bayesian models was similar(p> 0.01),while there were significant differences(p <0.01) between the DFA model and the other models.This study demonstrated that the Ba

关 键 词：SEDIMENT fingerprinting SEDIMENT source contribution Walling-Collins MODEL BAYESIAN MODEL DISCRIMINANT function analysis 

分 类 号：TV1[水利工程]