Comparison of the Artificial Intelligence Techniques and the Muskingum Methods in Flood Routing Estimation

Flood routing is one of the most important issues in river engineering because of prediction of the ascent and descent of flood hydrograph. As flood is a variable and unsteady flow، its routing requires accurate and detailed data collection at hydrometry stations. The Muskingum models very offer a useful procedure among the flood routing methods. Moreover، application of the artificial intelligence methods have grown substantially in the different water engineering and watershed modeling endeavors in recent decades. In the present study، the data collected by Wilson، Wu et al.، and veiss man Jr. and Lewis for three different rivers in the U. S. A were used for the flood routing processes using the Muskingum، artificial neural network، adaptive neuro-fuzzy inference system، and genetic programming. Simulation results of flood routing process using the mentioned methods were compared using the statistical indicators of R2، RMSE and MBE. The results indicated that the artificial intelligence methods were superior to the Muskingum method due to their lower RMSE. The RMSE value for the artificial intelligence techniques was 0. 00174 and for the Muskingum method it was 28. 727. The Muskingum method was not successful in flood hydrograph simulation with multi peaks. Despite the slight differences in accuracy estimation and error values ​​in the models، the artificial neural networks proved their superiority with the highest R2، and lowest RMSE and MBE. The adaptive neuro-fuzzy inference system and genetics programming were placed in next levels. Based on the ease of use and more accurate results، the use of artificial intelligence methods is recommended for further studies in this region.