A machine code-based genetic programming for suspended sediment concentration estimation

Correct estimation of suspended sediment concentration carried by a river is very important for many water resources projects. The application of linear genetic programming (LGP), which is an extension to genetic programming (GP) technique, for suspended sediment concentration estimation is proposed in this paper. The LGP is compared with those of the adaptive neuro-fuzzy, neural networks and rating curve models. The daily streamflow and suspended sediment concentration data from two stations, Rio Valenciano Station and Quebrada Blanca Station, operated by the US Geological Survey (USGS) are used as case studies. The root mean square errors (RMSE) and determination coefficient (R2) statistics are used for evaluating the accuracy of the models. Comparison of the results indicated that the LGP performs better than the neuro-fuzzy, neural networks and rating curve models. For the Rio Valenciano and Quebrada Blanca Stations, it is found that the LGP models with RMSE = 44.4 mg/l, R2 = 0.910 and RMSE = 13.9 mg/l, R2 = 0.952 in test period is superior in estimating daily suspended sediment concentrations than the best accurate neuro-fuzzy model with RMSE = 52.0 mg/l, R2 = 0.876 and RMSE = 17.9 mg/l, R2 = 0.929, respectively.