Comparative evaluation with the analytical hierarchy procedure of the Kalinske, Brooks, and Toffaleti equations used to estimate suspended-sediment discharges in rivers

Sedaei, Nazila; Salajegheh, Ali

doi:10.1504/ijw.2015.067449

Cited by 1 publication

(1 citation statement)

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“…Thus, researchers have needed to design appropriate alternative means of SS estimation, which has resulted in the development of several types of methods. For example, the Sediment Rating Curve (SRC) models, physically based models and numerical models are used in estimating sediment load (Sedaei and Salajegheh, 2015;Fadaee et al, 2022;Khosravi et al, 2022). Recently, machine learning approaches based on the concept of human behavior have also been applied.…”

Section: Introductionmentioning

confidence: 99%

A brief review of recent global trends in suspended sediment estimation studies

Hefzul Bari,

Yokoo,

Leong

2024

Hydrological Research Letters

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Sediment has the potential to influence the landscape, economy and way of life. If the estimation is accurate and the distribution characteristics are known, sediment can be used as a resource. In this brief review, we evaluated recent advances in suspended sediment estimation techniques between 2011 and 2022. The three most popular techniques are Sediment Rating Curves, Machine Learning Models and Physical and/or Numerical Models. Their potential and limitations are briefly explored. Our findings indicate that Machine Learning Models perform better, whereas Sediment Rating Curves offer flexibility in field applications. On the other hand, the paucity of necessary highresolution data for model calibration and validation hinders the performance of Physical and/or Numerical Models. The sampling frequency of input sediment data emerges as a pivotal factor influencing the performance of all methods. To overcome this issue, a semi-automatic surrogate method might be useful. Also, particle size could be included in the rating curves to obtain additional information. This is especially valuable for understanding sediment dynamics and the process of delta formation.

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Section: Introductionmentioning

confidence: 99%