Sediment transport modelling in an alluvial river with artificial neural network

Abstract

Simulation of flow and transport of sediment in alluvial rivers, particularly during release of water through dams, is a complex problem. Such simulations are generally carried out using various numerical models, subject to availability of requisite morphological and sediment data sets. In addition, various simplifying assumptions, quite often, are part and parcel of most of the available numerical sediment transport models. As such, non-compliance of any of the assumptions or non-availability of all the requite data sets restricts meaningful use of any numerical simulator in estimation of morphological changes in alluvial rives. This study attempts to resolve such issues associated with numerical models by using Artificial Neural Networks (ANN). The primary objective of the study is to explore and identify best performing ANN models in estimating morphological changes in alluvial rivers. In the present study, the one dimensional hydrodynamic modelling system, HEC-RAS (Hydrologic Engineering Centre’s River Analysis System), is employed for generating database to train multilayer feedforward networks (MLFN). Performance evaluations of all the developed MLFN models are made by using three statistical parameters, i.e. Mean Square Error (MSE), Mean Absolute Error (MAE) and Coefficient of Determination (R2). The study reveals that there are multiple ANN models, stretching across different training algorithms and transfer functions, which can be reliably used in developing sediment transport models in alluvial rivers.