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  • 标题:A Review on Applications of Artificial Intelligence-Based Models to Estimate Suspended Sediment Load
  • 本地全文:下载
  • 作者:Vahid Nourani
  • 期刊名称:International Journal of Soft Computing & Engineering
  • 电子版ISSN:2231-2307
  • 出版年度:2014
  • 卷号:3
  • 期号:6
  • 页码:121-127
  • 出版社:International Journal of Soft Computing & Engineering
  • 摘要: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
  • 关键词:Artificial Intelligence; Hybrid models; Suspended sediment load.
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