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The sedimentation in coastlines, waterways, and reservoirs is an environmental problem that can be coped with sediment removal systems such as hydro-suction. In this study, the Mayfly Algorithm (MA) is applied to predict the maximum depth ( h s) and diameter ( D s) of the formed scour hole attributed to the hydro-suction. Two machine learning methods including ANFIS and SVR were trained by the MA and Genetic Algorithm (GA) based on the measured data from this research as well as gathered data from pertinent resources. The most accurate methods in predicting  h sand  D swere ANFIS-MA and SVR-MA, respectively. The results showed that the MA improved the efficiency of ANFIS and SVR about 23.69% and 13.56%, whereas the performance enhancement for GA were about 17.06% and 7.98%, respectively. Comparing the results between the GA and MA demonstrated that the developed MA-embedded integrative methods are more accurate than the GA-embedded methods.

Application of mayfly algorithm for prediction of removed sediment in hydro-suction dredging systems