IMHE OpenIR  > Journal of Mountain Science  > Journal of Mountain Science-2016  > Vol13 No.12
Hybrid denoising-jittering data processing approach to enhance sediment load prediction of muddy rivers
Afshin PARTOVIAN; Vahid NOURANI; Mohammad Taghi ALAMI
Corresponding AuthorVahid NOURANI
2016-12
Source PublicationJournal of Mountain Science
ISSN1672-6316
Volume13Issue:12Pages:2135-2146
Subtype期刊论文
AbstractSuccessful modeling of hydro-environmental 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 runoff-sediment modeling of the case study up to 34% and 25% in the verification phase, respectively.
KeywordRunoff–sediment Modeling Ann Anfis Wavelet Denoising Jittered Data Minnesota River
DOI10.1007/s11629-016-3884-8
Indexed BySCI
Language英语
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/17826
CollectionJournal of Mountain Science_Journal of Mountain Science-2016_Vol13 No.12
Recommended Citation
GB/T 7714
Afshin PARTOVIAN,Vahid NOURANI,Mohammad Taghi ALAMI. Hybrid denoising-jittering data processing approach to enhance sediment load prediction of muddy rivers[J]. Journal of Mountain Science,2016,13(12):2135-2146.
APA Afshin PARTOVIAN,Vahid NOURANI,&Mohammad Taghi ALAMI.(2016).Hybrid denoising-jittering data processing approach to enhance sediment load prediction of muddy rivers.Journal of Mountain Science,13(12),2135-2146.
MLA Afshin PARTOVIAN,et al."Hybrid denoising-jittering data processing approach to enhance sediment load prediction of muddy rivers".Journal of Mountain Science 13.12(2016):2135-2146.
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