| description abstract | Most of the hydrological models developed and used previously in sediment yield modeling are complex and lack general applicability. Moreover, the availability of sediment data for the development and calibration of such models is very scarce. Therefore, there is a need to study and develop models that can handle easily measurable parameters of a particular site, even with short length. In recent years, multidisciplinary artificial intelligence techniques—namely, artificial neural networks (ANNs)—have shown the capability to solve such complex nonlinear systems. This study investigates the suitability of an inductive group method of data handling polynomial neural network (GMDH-NN) technique in estimating sediment yield. The data on various meteorological and geomorphological features—namely, river length, watershed area, erodible area, average slope of watershed, annual average rainfall, and drainage density—from 20 subwatersheds of the Arno River Basin in Italy were used for model development. The results of this study show that the inductive GMDH-NN can efficiently capture the trend of sediment yield with a coefficient of correlation of 0.975, even with this small data set. | |
