Developing nonlinear models for sediment load estimation in an irrigation canal

The study was performed to estimate the weekly sediment load in Thal canal located in Mianwali district Punjab, Pakistan. Past records of sediments and discharge have been considered as the input parameters. The best input combinations have been identified with the help of advanced algorithms including full, sequential and increasing embedding, genetic algorithm and hill climbing in combination with the gamma test. Model training has been carried out using two artificial neural network-based algorithms, namely Broyden–Fletcher–Goldfarb–Shanno (BFGS), back-propagation and a local linear regression technique. A variety of statistical parameters including R square, root mean squared error, mean square error and mean bias error (MBE) has been calculated in order to evaluate the best models. The results strongly suggested that BFGS-based model performed better than all other models with remarkably low values of MBE. Significantly high values of correlation coefficient (R square) in both training and testing evidenced a close similarity between actual and predicted sediment load values for the same model.