A performance evaluation of neuro-fuzzy and regression methods in estimation of sediment load of selective rivers

Sediment rating curves (SRCs) have been recognized as the most popular method for estimating sediment in the hydrology of river sediments and in watersheds. In this regard, in order to compare and correct estimation methods of river sediment load, estimated rates of several univariate types of SRCs...

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Bibliographic Details
Published in:Acta geophysica 2019-02, Vol.67 (1), p.205-214
Main Authors: Varvani, J., Khaleghi, M. R.
Format: Article
Language:English
Subjects:
Artificial neural networks
Climatic zones
Data analysis
Earth and Environmental Science
Earth Sciences
Fluvial sediments
Fuzzy logic
Geophysics/Geodesy
Geotechnical Engineering & Applied Earth Sciences
Hydrologic models
Hydrology
Hydrometric stations
Load distribution
Performance evaluation
Performance indices
Regression analysis
Regression models
Research Article - Hydrology
River sediments
Rivers
Sediment load
Sediments
Stations
Structural Geology
Watersheds
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Summary:Sediment rating curves (SRCs) have been recognized as the most popular method for estimating sediment in the hydrology of river sediments and in watersheds. In this regard, in order to compare and correct estimation methods of river sediment load, estimated rates of several univariate types of SRCs and a multivariate type of SRCs (MSRCs) were studied using the neuro-fuzzy and tree regression models in five selective hydrometric stations of different climatic zones of Iran and with various indexes of the accuracy (AI) and the precision (PI). The results of the data analysis showed that the mean of the AI of neuro-fuzzy and tree regression models in selective stations is 151 and 536%, respectively, which shows the low efficiency compared with SRCs. Also according to the results, the best rate of the AI of the MSRCs belongs to the Glink station with the rate of 1.12. Also, the average value of the AI of MSRCs is 1.15 which is an acceptable amount of the other considered various methods.
ISSN:1895-6572
1895-7455
DOI:10.1007/s11600-018-0228-9