Assessing Soft Computing Techniques for River Suspended Sediment Estimation

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 ۵ types of data mining models in simulating river sediment load was investigated, then the results of the ۵ 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 R۲=۰.۹۸, RMSE=۰.۷۴ and MBE=۰.۰۰۰۴۷ has better performance than the SVM, ANFIS, MARS and GMDH models. The SRC method had the lowest R square value ۰.۶۱ and average RMSE ۷۵ and MBE ۲۰. In general, all five data mining methods showed better performance than the SRC sediment ranking curve.

کلیدواژه ها:

suspended load ، Fuzzy Neural Network ، Sedimentation ، Gene expression programming ، support vector machine. Gene ، expression programming