A non-linear rainfall-runoff model using radial basis function network

Lin, Gwo‐Fong; Chen, Lu-Hsien

doi:10.1016/j.jhydrol.2003.10.015

Cited by 146 publications

(74 citation statements)

References 10 publications

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“…The RBF, which is multilayer and feed-forward, is often used for strict interpolation in multi-dimensional space. The term "feed-forward" means that the neurons are organized as layers in a layered neural network [26]. The basic architecture of a three-layered neural network is shown in Figure 2.…”

Section: Radial Basis Functionmentioning

confidence: 99%

“…In the structure of RBF network, the input data, x, is a p-dimensional vector, which is transmitted to each hidden unit. The activation function of hidden units is symmetric in the input space, and the output of each hidden unit depends only on the radial distance between the input vector, x, and the center for the hidden unit [26]. Each node in the hidden layer is a p-multivariate Gaussian function, given as follows:…”

Section: Radial Basis Functionmentioning

confidence: 99%

“…Thus, the solution should be approximated to reduce the number of PEs in the hidden layer and cleverly position them over the input space regions. This entails the need to estimate the position of each radial basis function and its variance, as well as to compute the linear weights, w i [21,26]. An unsupervised technique, known as the k-nearest neighbor rule, is used to estimate the mean and the variance.…”

Section: Radial Basis Functionmentioning

confidence: 99%

See 2 more Smart Citations

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: Radial Basis Functionmentioning

confidence: 99%

Section: Radial Basis Functionmentioning

confidence: 99%

Section: Radial Basis Functionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…It records as a ratio the level of overall agreement between the observed and modelled datasets and is a popular metric that is often expressed in percentage terms using different phrasing e.g. "Error in Volume" (Rajurkar et al, 2004); "Error of Total Runoff Volume" (EV; Lin and Chen, 2004); "Percent Bias" (PBIAS; Yapo et al, 1996;Yu and Yang, 2000); "Deviation of Runoff…”

Section: Equation 18mentioning

confidence: 99%

HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts

Dawson

Abrahart

See

2007

Environmental Modelling & Software

448

244

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“…In recent decades, artificial neural networks (ANNs) have become a well-known tool for hydrologic forecasting [18][19][20][21][22][23][24][25][26][27][28][29]. However, ANNs require a large amount of hydrologic data to determine the adaptive weights, which are inadequate to be applied to data-sparse areas.…”

Section: Introductionmentioning

confidence: 99%

Grey Forecast Rainfall with Flow Updating Algorithm for Real-Time Flood Forecasting

Lee

2015

Water

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Abstract:The dynamic relationship between watershed characteristics and rainfall-runoff has been widely studied in recent decades. Since watershed rainfall-runoff is a non-stationary process, most deterministic flood forecasting approaches are ineffective without the assistance of adaptive algorithms. The purpose of this paper is to propose an effective flow forecasting system that integrates a rainfall forecasting model, watershed runoff model, and real-time updating algorithm. This study adopted a grey rainfall forecasting technique, based on existing hourly rainfall data. A geomorphology-based runoff model can be used for simulating impacts of the changing geo-climatic conditions on the hydrologic response of unsteady and non-linear watershed system, and flow updating algorithm were combined to estimate watershed runoff according to measured flow data. The proposed flood forecasting system was applied to three watersheds; one in the United States and two in Northern Taiwan. Four sets of rainfall-runoff simulations were performed to test the accuracy of the proposed flow forecasting technique. The results indicated that the forecast and observed hydrographs are in good agreement for all three watersheds. The proposed flow forecasting system could assist authorities in minimizing loss of life and property during flood events. OPEN ACCESSWater 2015, 7 1841

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A non-linear rainfall-runoff model using radial basis function network

Cited by 146 publications

References 10 publications

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

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

HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts

Grey Forecast Rainfall with Flow Updating Algorithm for Real-Time Flood Forecasting

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