Application of trend analysis and artificial neural networks methods: The case of Sakarya River

Ceribasi G., Dogan E., Akkaya U., Kocamaz U. E.

SCIENTIA IRANICA, vol.24, no.3, pp.993-999, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 24 Issue: 3
  • Publication Date: 2017
  • Doi Number: 10.24200/sci.2017.4082
  • Journal Name: SCIENTIA IRANICA
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.993-999
  • Keywords: Trend analysis, Artificial neural networks, Sakarya river, Rainfall, Stream flow, Suspended load
  • Bursa Uludag University Affiliated: Yes


Various artificial intelligence techniques are used in order to make prospective estimations with available data. The most common and applied method among these artificial intelligence techniques is Artificial Neural Networks (ANN). On the other hand, another method which is used in order to make prospective estimations with available data is Trend Analysis. When the relation of these two methods is analyzed, Artificial Neural Networks method can present the prospective estimation numerically, while there is no such a case in Trend Analysis. Trend Analysis method presents result of prospective estimation as a decrease or increase in data. Therefore, it is quite important to make a comparison between these methods which brings about prospective estimation with the available data, because these two methods are used in most of these studies. In this study, annual average stream flow and suspended load measured in Sakarya River along with average annual rainfall trend were analyzed with trend analysis method. Daily, weekly, and monthly average stream flows and suspended loads measured in Sakarya River and average daily, weekly, and monthly rainfall data of Sakarya were all analyzed by ANN Model. Results of trend analysis method and ANN model were compared. (C) 2017 Sharif University of Technology. All rights reserved.