Estimation of rivers sediment load is one of the most important problems for designof hydraulic structures, investigating water quality, conserving fish habitate, estimatingerosion and determining watershed management effects.There is two metheod for estimating sediment More
Estimation of rivers sediment load is one of the most important problems for designof hydraulic structures, investigating water quality, conserving fish habitate, estimatingerosion and determining watershed management effects.There is two metheod for estimating sediment load: empirical and regressionmethods. Existence of numerous empirical methods for estimation of river sedimentload, a wide range of calibration coefficients shows that a suitable analytical orempirical method does not yet exist to accurately estimate the sediment load.Therefore, the measured discharges and sediment concentrations in hydrometrystations are statistically analyses for an accurate estimation of sediment loads in rivers.In usual statistical methods a power function is generally fitted on the data sets of flowand sediment discharge and thus the total sediment load could be calculated using thisfunction. These methods are not able to recognize and separate the specific datameasuring conditions. Therefore, they are not only able to accurately estimate thesediment load, but also can not show the temporal variation of sediment loads. In spiteof this problems, researcher are using Artificial Intelligence methods such as FuzzyLogic, nowadays.In this study, the measured suspended sediment load at hydrometry stations ofTelvar River is analyzed using USBR and FAO methods (usual statistical methods).Furthermore, Sediment suspended load are estimated with a model developed based onFuzzy Logic rules. Then the results of these mothods are compared. This researchstudy has shown that the temporal variation of sediment loads can be analyzed using afuzzy method. Also the, results obtained using the fuzzy method in comparison withthe corresponding values obtained using the usual statistical methods shows a bettercorrelation with the observed values. In all stations the fuzzy method estimated thesediment loads between ones obtained using statistical methods.
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Sediment transport has always affected the river and civil structures and the lack of knowledge about its exact amount causes high damages. Therefore, it is very important to properly estimate the sediment load in rivers in terms of sediment, erosion and flood control. More
Sediment transport has always affected the river and civil structures and the lack of knowledge about its exact amount causes high damages. Therefore, it is very important to properly estimate the sediment load in rivers in terms of sediment, erosion and flood control. This study used two new data mining methods including M5 model tree and support vector regression comparing with the classic method of sediment rating curve to estimate the suspended sediment load in Aharchay River. To assess the performance of the used methods, three criteria including the correlation coefficient, root mean square error and mean absolute error were used. Analyzing the sensitivity of models to the input variables, it was found that the variable of flow discharge in the current month had the greatest effect on the amount of suspended sediment load. The results showed the high accuracy of new data mining methods in comparison with the sediment rating curve. Although, both considered data mining methods had more accuracy and less error compared to the conventional sediment rating curve, given the simple understandable linear relationships provided by the M5 model tree, this method is recommended for similar cases.
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Estimation of the sediment loads in a stream flow is one of the well-discussed topics in the sediment hydraulics literature. In almost the entire river engineering projects addressing issues such as river morphology, river bed changes, estimation of the annual sediment More
Estimation of the sediment loads in a stream flow is one of the well-discussed topics in the sediment hydraulics literature. In almost the entire river engineering projects addressing issues such as river morphology, river bed changes, estimation of the annual sediment deposition at the river intakes, banks protection, dredging of the water channels, etc are of prime importance. The amount of sediment transported through a river cross-section may be under a mix of various influences from flow depths and widths, energy slope, temperature and fluid characteristics, flow turbulence and a wide range of their sizes, forms, densities, sediment particle concentrations, and other physical and hydraulic properties. The variety of formulae applied in sediment transport and estimation calculations also the paucity of information and data on the performance of such formulae makes the selection of the suitable ones for different specific conditions an extremely difficult task. Any choice in this regard should incorporate the evaluation and analysis of river hydraulics. In the present paper, several sediment estimation formulae examined for Greater Karun River along Mollasani-Farsiat reach with the purpose of proposing the best sediment transport relation to be applied in later researches. Results showed flow-based energy-based methods are better suited for the Karoon River.
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