ABSTRACT
The present study is a real-time application of Artificial Neural Networks (ANNs) to estimate, predict and forecast the suspended sediment transport in streams and river systems using time series data. NN approach is a crucial and readily adaptable important methodology when hydrograph/unit hydrograph methodology and conventional mathematics are inconvenient to apply for an emergency situation to act as the sediment runoff time series data is highly complex in nature i.e. situations like availability of less amount of data, sometimes the very high and huge quantity of data, poor quality of data, erratic nature of data, need of emergency prediction and forecasting, etc. The present study is pertaining to a tributary; namely Peddavagu in the Godavari river system in India. A back propagation algorithm was employed in the methodology among the layers of multi-layered feed-forward neural network with one hidden layer and two hidden layer approaches. The study also demonstrates the beauty of an application of computational intelligence/ artificial intelligence technique as how it is superior to the conventional mathematical modeling. Sediment rating curve (SRC), mathematical modeling was carried out to present a comparative performance of the applied methodology. Statistical performance evaluation criteria using root mean square error (RMSE), correlation coefficient (CC) and coefficient of determination (DC) comprehend the sensitivity in applying the ANNs for complex suspended sediment time series to predict and forecast the sediment hydrology of river systems. Hysteresis effect of sediment runoff is also a component in the study to reveal a result of many number of sediment concentration magnitudes for a specific runoff magnitude in the river flow system.
