A Review on Applications of Artificial Intelligence-Based Models to Estimate Suspended Sediment Load Vahid Nourani
Vahid Nourani, Department of Civil Eng., University of Tabriz, Tabriz, Iran.
Manuscript received on December 08, 2014. | Revised Manuscript received on December 15, 2014. | Manuscript published on January 05, 2014. | PP: 121-127  | Volume-3 Issue-6, January 2014. | Retrieval Number: F2009013614/2014©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Undeniably application of Artificial Intelligence (AI) has grown increasingly through past years. Hydrology also has its portion of utilization of AI-based models. Among different parts of hydrology, Suspended Sediment Load (SSL) estimation plays an important role since SSL can cause trouble in water resources engineering and environmental procedures. Therefore, employing AI-based models would cause more precise consequences. Recently proposed hybrid models provided more accurate prediction. These models employ AI-based models too, but in comparison, hybrid models forecast phenomena more accurate than sole AI-based models. It is because hybrid models can deal with non-stationary data. In this paper, advantages and disadvantages of both AI-based and hybrid models in the field of SSL modeling are discussed in the details
Keywords: Artificial Intelligence, Hybrid models, Suspended sediment load.