Development of predictive model for flood routing using genetic expression programming

Flood disasters continue to occur in many countries around the world and cause tremendous casualties and property damage. Flood peak values are required in the design of bridges, culvert waterways, spillways for dams, and estimation of scour at a hydraulic structure. When a flood wave passes through a reservoir its peak is attenuated, and the time base is enlarged due to effect of storage. Modification in the hydrograph is studied through flood routing. Flood routing is the technique of determining the flood hydrograph at a section of a river by utilising the data of flood flow at one or more upstream sections. Advances in the field of artificial intelligence (AI) offer opportunities for utilising new algorithms and models. Experiments have shown that the gene-expression programming (GEP) model and its algorithms are 100-60 000 times faster than older genetic algorithms. This study presents the GEP method, which is an extension to genetic programming, as an alternative approach to modelling of flood routing in natural channels. Thus, novel models for prediction of flood routing using the GEP technique are developed. The proposed GEP-based approach is compared with three hydrograph examples and the results of other solution techniques. The GEP method makes use of few hydrologic parameters (inflow, outflow, and time). The performance of the models is evaluated by two goodness-of-fit measures, namely, the root-mean-square error (RMSE) and the coefficient of determination (R-2). In the results, the GEP models show superior performance to the other solution techniques based on the Muskingum model.