Estimating total sediment transport in a small, mountainous Mediterranean river: The case of Vouraikos River, NW Peloponnese, Greece

2021 ◽  
Author(s):  
Sotirios Karalis ◽  
Efthimios Karymbalis ◽  
Konstantinos Tsanakas
Keyword(s):  
Sediment Transport ◽  
Mediterranean River ◽  
Total Sediment

A comparison of two total sediment transport models for rivers

2016 ◽  
pp. 776-779
Author(s):  
Vipinkumar Yadav ◽  
S.M. Yadav ◽  
Sahita I. Waikhom
Keyword(s):  
Sediment Transport ◽  
Transport Models ◽  
Total Sediment

scholarly journals A Fuzzy Transformation of the Classic Stream Sediment Transport Formula of Yang

Water ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 257
Author(s):  
Konstantinos Kaffas ◽  
Matthaios Saridakis ◽  
Mike Spiliotis ◽  
Vlassios Hrissanthou ◽  
Maurizio Righetti
Keyword(s):  
Sediment Transport ◽  
Linear Regression ◽  
Regression Model ◽  
Sediment Concentration ◽  
Stream Sediment ◽  
Fuzzy Regression ◽  
Large Set ◽  
Stream Power ◽  
Fuzzy Transformation ◽  
Total Sediment

The objective of this study is to transform the arithmetic coefficients of the total sediment transport rate formula of Yang into fuzzy numbers, and thus create a fuzzy relationship that will provide a fuzzy band of in-stream sediment concentration. A very large set of experimental data, in flumes, was used for the fuzzy regression analysis. In a first stage, the arithmetic coefficients of the original equation were recalculated, by means of multiple regression, in an effort to verify the quality of data, by testing the closeness between the original and the calculated coefficients. Subsequently, the fuzzy relationship was built up, utilizing the fuzzy linear regression model of Tanaka. According to Tanaka’s fuzzy regression model, all the data must be included within the produced fuzzy band and the non-linear regression can be concluded to a linear regression problem when auxiliary variables are used. The results were deemed satisfactory for both the classic and fuzzy regression-derived equations. In addition, the linear dependence between the logarithmized total sediment concentration and the logarithmized subtraction of the critical unit stream power from the exerted unit stream power is presented. Ultimately, a fuzzy counterpart of Yang’s stream sediment transport formula is constructed and made available to the readership.

Sediment transport to the Arctic Ocean and adjoining cold oceans*

2006 ◽  
Vol 37 (4-5) ◽  
pp. 413-432 ◽  
Author(s):  
Bent Hasholt ◽  
Nelly Bobrovitskaya ◽  
Jim Bogen ◽  
James McNamara ◽  
Sebastian H. Mernild ◽  
...  
Keyword(s):  
Sediment Transport ◽  
Arctic Ocean ◽  
Monitoring Network ◽  
High Arctic ◽  
Sediment Flux ◽  
The Arctic ◽  
Sediment Loads ◽  
The Arctic Ocean ◽  
Available Information ◽  
Total Sediment

This paper reviews and synthesises available information on sediment transport to the Arctic Ocean and adjoining seas with open contact to the Atlantic and Pacific Oceans. Special emphasis is placed on calculation and estimation of the sediment flux from the mostly ungauged high Arctic areas on the American continent, in Greenland, and on islands in the Arctic Ocean, and from Russia. In the absence of reliable information on bedload fluxes for most rivers, attention is directed primarily to suspended sediment loads. By combining available monitoring data and estimates for ungauged areas, the total sediment transport to the Arctic Ocean is estimated to be 324–884 × 106 t yr−1. Of this total, a maximum of about 56% can be considered as monitored, while the rest is based on different types of estimate. It is clearly demonstrated that the monitoring network in the high Arctic is inadequate and that there is a lack of knowledge concerning the proportion of the load that actually reaches the sea, as well as bedload.

scholarly journals Appraising the Impact of Naraj Barrage on Sedimentation of Chilika Lagoon; the Soft Computing Model for Prediction

2020 ◽  
pp. 31-41
Author(s):  
Siba Prasad Mishra ◽  
Ananta Charan Ojha
Keyword(s):  
Sediment Transport ◽  
Suspended Sediment ◽  
Learning Algorithm ◽  
River System ◽  
Suspended Sediment Transport ◽  
Chilika Lagoon ◽  
Surrounding Environment ◽  
Ensemble Machine Learning ◽  
The Impact ◽  
Total Sediment

Estimation of suspended sediment transport in a catchment area is very important to manage water resources, construction of dam and barrage, as well as to protect the surrounding environment. The daily monsoon sediment and flow were observed physically and quantity of total sediment input by the two major rivers of the south Mahanadi deltaic rivers to Lagoon Chilika were calculated during pre Naraj barrage (FY 2000 to 2003) and post Naraj Barrage period (FY’s 2004, 2012, 2013) establishing an observatory in the rivers the Daya and the Bhargovi.[b] The non-linear complex relationship between quantity of suspended sediment transport and volume of river-discharge inflicts challenge to the estimation process. In this paper, two southern-most distributaries, the Daya and the Bhargovi of the Mahanadi River System which flow into Chilika lagoon are studied. Random Forest, an ensemble machine learning algorithm is used to estimate the transport of sediment by these two distributaries using predictive modeling. Predicted figures based on the gathered data from these distributaries during pre-barrage period 2000-2003 have been compared with the observed data gathered in post-barrage years 2004, 2012 and 2013. Comparative data suggests that the construction of Naraj barrage has significantly reduced the concentration of sediment influx into Chilika lagoon while controlling the discharge through effective barrage management.

Sign in / Sign up

Export Citation Format

Share Document