Estimation of scour depth downstream of sudden diverging side walls channels via SVM and ANFIS

An accurate estimation of scour depth around hydraulic structures like channels with sudden diverging side walls is of great importance. Numerous of studies has been done about scouring at downstream of hydraulic structure. Therefore, there are several semi-empirical equations to predict scour depth, however, acceptable results have not been provided yet. In the current paper, the performance of the Support Vector Machine (SVM) and Adapted Neural Fuzzy Inference System (ANFIS) approaches were assessed in scour depth prediction at downstream of sudden diverging side walls channels with sill. According to the obtained results, the SVM and ANFIS models were found to be more reliable. The results indicated that in estimating the scour depth at downstream of sudden diverging side walls channels with artificial intelligence approaches a good agreement could be seen between observed and predicted values

Estimation of scour depth downstream of sudden diverging side walls channels via SVM and ANFIS Keywords:

Diverging side walls , SVM , sill , Scour depth