Neural-Network Models of Rainfall-Runoff Process

Smith, Jason; Eli, Robert N.

doi:10.1061/(asce)0733-9496(1995)121:6(499)

Cited by 273 publications

(114 citation statements)

References 11 publications

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“…The ANN is a powerful soft computational technique which has been widely used in many areas of water resource management and environmental sciences [5][6][7][8][9][10][11][12][13][14][15]. ANN comprises parallel systems that are composed of Processing Elements (PE) or neurons, which are assembled in layers and connected through several links or weights.…”

Section: Introductionmentioning

confidence: 99%

Comparison between Multi-Layer Perceptron and Radial Basis Function Networks for Sediment Load Estimation in a Tropical Watershed

Memarian¹,

Balasundram

2012

JWARP

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Prediction of highly non-linear behavior of suspended sediment flow in rivers has prime importance in environmental studies and watershed management. In this study, the predictive performance of two Artificial Neural Networks (ANNs), namely Radial Basis Function (RBF) and Multi-Layer Perceptron (MLP) were compared. Time series data of daily suspended sediment discharge and water discharge at the Langat River, Malaysia were used for training and testing the networks. Mean Square Error (MSE), Normalized Mean Square Error (NMSE) and correlation coefficient (r) were used for performance evaluation of the models. Using the testing data set, both models produced a similar level of robustness in sediment load simulation. The MLP network model showed a slightly better output than the RBF network model in predicting suspended sediment discharge, especially in the training process. However, both ANNs showed a weak robustness in estimating large magnitudes of sediment load.

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Section: Introductionmentioning

confidence: 99%

Comparison between Multi-Layer Perceptron and Radial Basis Function Networks for Sediment Load Estimation in a Tropical Watershed

Memarian¹,

Balasundram

2012

JWARP

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“…In an application using two neural networks, Zhu et al [8] predicted upper and lower bounds on the flood hydrograph in Butter Creek, New York. Smith [9] used a back-propagation ANN model to predict the peak discharge and the time to peak resulting from a single rainfall pattern. Carriere et al [10] designed and developed a Virtual Runoff Hydrograph System (VROHS) based on ANN to generate runoff hydrograph.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks for Event Based Rainfall-Runoff Modeling

Sarkar¹,

Kumar²

2012

JWARP

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The Artificial Neural Network (ANN) approach has been successfully used in many hydrological studies especially the rainfall-runoff modeling using continuous data. The present study examines its applicability to model the event-based rainfall-runoff process. A case study has been done for Ajay river basin to develop event-based rainfall-runoff model for the basin to simulate the hourly runoff at Sarath gauging site. The results demonstrate that ANN models are able to provide a good representation of an event-based rainfall-runoff process. The two important parameters, when predicting a flood hydrograph, are the magnitude of the peak discharge and the time to peak discharge. The developed ANN models have been able to predict this information with great accuracy. This shows that ANNs can be very efficient in modeling an event-based rainfall-runoff process for determining the peak discharge and time to the peak discharge very accurately. This is important in water resources design and management applications, where peak discharge and time to peak discharge are important input variables.

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“…Recently, numerous ANN-based rainfall-runoff models have been proposed to forecast streamflow [2][3][4][5] and water quality [6][7][8][9]. Neuro-fuzzy computing using hybrid learning algorithms has been proposed for modeling time series [10,11].…”

Section: Introductionmentioning

confidence: 99%