Dual Artificial Neural Network for Rainfall-Runoff Forecasting

Dual Artificial Neural Network for Rainfall-Runoff Forecasting

One of the principal issues related to hydrologic models for prediction of runoff is the estimation of extreme values (floods). It is well understood that unless the models capture the dynamics of rainfall-runoff process, the improvement in prediction of such extremes is far from reality. In this paper, it is proposed to develop a dual (combined and paralleled) artificial neural network (D-ANN)...

Daily Runoff Forecasting using Artificial Neural Network

Rainfall-Runoff is the most important hydrological variables used in most of the water resources applications. Watershed based planning and management requires thorough understanding hydrological process and accurate estimation of runoff. An Artificial Neural Network (ANN) methodology was employed to forecast daily runoff for the Kadam watershed of G-5 sub-basin of Godavari river basin. On the ...

Artificial Neural Network Model for Rainfall-Runoff Relationship

Conceptual models have been widely used and are considered to be the best choice for describing the runoff process in a watershed. In most cases, the solution accuracy is mainly based on the topographic and hydrologic information subject to certain requirements for model calibration. Thus, these types of model are inappropriate for watershed area with little hydrologic data. Artificial neural n...

Single-Model-Bootstrap Applied to Neural Network Rainfall-Runoff Forecasting

Most neural network hydrological modelling has used split-sample validation to ensure good out-of-sample generalisation and thus safeguard each potential solution against the danger of overfitting. However, given that each sub-set is required to provide a comprehensive and sufficient representation of both environmental inputs and hydrological processes, then to partition the data could create ...

Rainfall-runoff model usingan artificial neural network approach