Assessing Soft Computing Techniques for River Suspended Sediment Estimation

Document Type : Original Article

Authors

1 Soil Conservation and Watershed Management Research Department, Markazi Agricultural and Natural Resources Research and Education Center, Arak, Agricultural Research Education & Extention Organization (AREEO). Arak, Iran.

2 Department of Hydraulic Structures, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

3 Department of River and Coastal Engineering, Soil Conservation and Watershed Management Institute, Agricultural Research Education & Extention Organization (AREEO), Tehran, Iran.

4 Soil Conservation and Watershed Management Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran.

Abstract
Sediment load along with river flow causes irreparable damage to water development projects. Estimation of river sediment load is an important and practical issue in the study and design of water and hydraulic projects. The purpose of this research is to evaluate and compare adaptive neural-fuzzy models (ANFIS), (SVM), (GEP), (GMDH) and (MARS) and compare with the (SRC) method in estimating sediment load of Pol Doab station of Qarachay River, Markazi Province. For this purpose, the performance of 5 types of data mining models in simulating river sediment load was investigated, then the results of the 5 methods were compared with each other and with the results of the scale curve method. The results indicate the acceptable performance of data mining models compared to the scale curve. The results also showed that the GEP model with R2=0.98, RMSE=0.74 and MBE=0.00047 has better performance than the SVM, ANFIS, MARS and GMDH models. The SRC method had the lowest R square value 0.61 and average RMSE 75 and MBE 20. In general, all five data mining methods showed better performance than the SRC sediment ranking curve.

Keywords


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Volume 4, Issue 2
Special Issue: Guest Editor: Prof. Ozgur Kisi
September 2025
Pages 188-212