Hybrid denoising-jittering data processing approach to enhance sediment load prediction of muddy rivers
文献类型：期刊论文
| 作者 | Afshin PARTOVIAN; Vahid NOURANI; Mohammad Taghi ALAMI |
| 刊名 | Journal of Mountain Science
 |
| 出版日期 | 2016-12 |
| 卷号 | 13期号:12页码:2135-2146 |
| 关键词 | Runoff–sediment modeling ANN ANFIS Wavelet denoising Jittered data Minnesota River |
| ISSN号 | 1672-6316 |
| 通讯作者 | Vahid NOURANI |
| 英文摘要 | Successful 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. |
| 收录类别 | SCI |
| 语种 | 英语 |
| 源URL | [http://ir.imde.ac.cn/handle/131551/17826]  |
| 专题 | 成都山地灾害与环境研究所_Journal of Mountain Science _Journal of Mountain Science-2016_Vol13 No.12 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：成都山地灾害与环境研究所
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