Hybrid denoising-jittering data processing approach to enhance sediment load prediction of muddy rivers  

作　　者：Afshin PARTOVIAN Vahid NOURANI Mohammad Taghi ALAMI Afshin PARTOVIAN;Vahid NOURANI;Mohammad Taghi ALAMI(Department of Civil Engineering,Najafabad Branch,Islamic Azad University;Faculty of Civil Engineering,University of Tabriz)

机构地区：[1]Department of Civil Engineering,Najafabad Branch,Islamic Azad University [2]Faculty of Civil Engineering,University of Tabriz

出　　处：《Journal of Mountain Science》2016年第12期2135-2146,共12页山地科学学报（英文）

基　　金：financially supported by a grant from Research Affairs of Najafabad Branch,Islamic Azad University,Iran

摘　　要：Successful modeling of hydroenvironmental processes widely relies on quantity and quality of accessible data,and noisy data can affect the modeling performance.On the other hand in training phase of any Artificial Intelligence(AI) based model,each training data set is usually a limited sample of possible patterns of the process and hence,might not show the behavior of whole population.Accordingly,in the present paper,wavelet-based denoising method was used to smooth hydrological time series.Thereafter,small normally distributed noises with the mean of zero and various standard deviations were generated and added to the smooth time series to form different denoised-jittered data sets.Finally,the obtained pre-processed data were imposed into Artificial Neural Network(ANN) and Adaptive Neuro-Fuzzy Inference System(ANFIS)models for daily runoff-sediment modeling of the Minnesota River.To evaluate the modeling performance,the outcomes were compared with results of multi linear regression(MLR) and Auto Regressive Integrated Moving Average(ARIMA)models.The comparison showed that the proposed data processing approach which serves both denoising and jittering techniques could enhance the performance of ANN and ANFIS based runoffsediment modeling of the case study up to 34%and 25%in the verification phase,respectively.

关 键 词：Runoff-sediment modeling ANN ANFIS Wavelet denoising Jittered data Minnesota River 

分 类 号：TV149[水利工程—水力学及河流动力学]