Estimation of suspended sediment load using regression trees and model trees approaches (Case study: Hyderabad drainage basin in Iran). Issue 2 (4th May 2017)

• Record Type: Journal Article
• Authors: Talebi, Ali; Mahjoobi, Javad; Dastorani, Mohammad Taghi; Moosavi, Vahid
• Is Part Of: ISH journal of hydraulic engineering. Volume 23:Issue 2(2017)
• Page Start: 212
• Page End: 219
• Publication Date: 2017-05-04
• Subjects: Suspended sediment -- CART -- M5′ algorithm -- ANN -- sediment rating curve; Hydraulic engineering -- Periodicals; Hydraulic engineering -- India -- Periodicals; Hydraulic engineering; India; Periodicals; 627
• DOI: 10.1080/09715010.2016.1264894
• Languages: English
• ISSNs: 0971-5010

Abstract

Abstract: Estimation of suspended sediment load is one of the important topics in river engineering. Different methods are used for estimating the sediment rate. In recent years, different artificial intelligence (AI) methods, such as artificial neural network (ANN), have been used for the estimation of sediments in rivers. In this research, the suspended sediment load has been studied by using regression trees (RTs) and model trees (MTs). The study area has been located in Hyderabad watershed in west of Iran. The input data included the flow discharge, sum of three days discharge, sum of five days precipitation and the suspended sediment discharge were considered as output in the models. The numbers of total data of sediment discharge was 223 records. The obtained results were compared with ANN method (feed forward back propagation algorithm) and sediment rating curve (SRC). Results showed that RT and MT outperformed ANN method in the study area. The method of SRC had high accuracy for daily sediment discharge less than 100 ton per day in comparison with AI models, while the AI models had higher accuracy for high sediment discharge. Moreover, the combination of artificial intelligent models had high accuracy regarding to each model lonely.