Estimation of suspended sediment load in different time steps using hybrid wavelet-ANFIS

被引：3

作者：

Ardakani M.A.H. ^{[1
]}

Behnia N. ^{[1
]}

机构：

[1] Faculty of Natural Resources, Yazd University, Yazd

来源：

International Journal of Hydrology Science and Technology | 2018年 / 8卷 / 04期

关键词：

Adaptive neuro-fuzzy inference systems; ANFIS; Artificial intelligence; Modelling; Suspended sediment;

D O I：

10.1504/IJHST.2018.095548

中图分类号：

学科分类号：

摘要：

The aim of this study was to predict suspended sediment load for one and two months ahead using hybrid wavelet-ANFIS model. For this purpose, in the first step, the raw data were imposed to the ANFIS model and modelling was carried out. Afterwards, the data were decomposed at different levels and by different mother wavelets and the obtained coefficients were imported to the ANFIS model. The results indicate the significant impact of one-dimensional wavelet analysis on the performance of ANFIS model and the acceptable performance of hybrid wavelet-ANFIS in modelling sediment for one and two months ahead. The results also suggest reducing the accuracy of the model by increasing the time step from one to two months. Results showed that the hybrid wavelet-ANFIS model had the best performance for predicting sediment on month ahead with a modified correlation coefficient of 0.97 and RMSE of 0.71. Copyright © 2018 Inderscience Enterprises Ltd.

引用

页码：372 / 392

页数：20

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