Suitability of different neural networks in daily flow forecasting

Singh, Pankaj; Deo, M. C.

doi:10.1016/j.asoc.2006.05.003

Cited by 83 publications

(30 citation statements)

References 19 publications

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“…Scientists have been studying non-linear system modelling for years and they have succeeded in teaching non-linear system dynamics to artificial neural networks without any mathematical modelling [22,23,24]. Neural networks, with their remarkable ability to learn complicated relations from imprecise data can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques.…”

Section: Figure 1 -Erzurum's Highway and Sections Smentioning

confidence: 99%

An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey

Çodur¹,

Tortum²

2015

PROMET

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Section: Figure 1 -Erzurum's Highway and Sections Smentioning

confidence: 99%

An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey

Çodur¹,

Tortum²

2015

PROMET

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“…Gneiting and Raftery (2007) have formally demonstrated that the CRPS for probabilistic forecasts is equivalent to the mean absolute error (MAE) for single forecasts. This result is based on previous mathematical proofs by Baringhaus and Franz (2004) and by Székely and Rizzo (2005). It thus provides a convenient way to compare the performance of ensemble forecasts (mean CRPS) with the performance of single forecasts (MAE) for the same watershed.…”

Section: The Continuous Ranked Probability Scorementioning

confidence: 99%

“…Coulibaly et al, 1999;Maier and Dandy, 2000;Singh and Deo, 2007) is the multilayer perceptron (Rosenblatt, 1958). It is capable of learning any multivariate non-linear relationship between input and output values, if provided with a database of sufficient length and if satisfactory training is performed (Cybenko, 1989;Hornik et al, 1989).…”

Section: Introductionmentioning

confidence: 99%

An experiment on the evolution of an ensemble of neural networks for streamflow forecasting

Boucher¹,

Laliberté²,

Anctil³

2010

Hydrol. Earth Syst. Sci.

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Abstract. We present an experiment on fifty multilayer perceptrons trained for streamflow forecasting on three watersheds using bootstrapped input series. This type of neural network is common in hydrology and using multiple training repetitions (ensembling) is a popular practice: the information issued by the ensemble is then aggregated and considered to be the final output. Some authors proposed that the ensemble could serve the calculation of confidence intervals around the ensemble mean. In the following, we are interested in the reliability of confidence intervals obtained in such fashion and in tracking the evolution of the ensemble of neural networks during the training process. For each iteration of this process, the mean of the ensemble is computed along with various confidence intervals. The performance of the ensemble mean is evaluated based on the mean absolute error. Since the ensemble of neural networks resemble an ensemble streamflow forecast, we also use ensemble-specific quality assessment tools such as the Continuous Ranked Probability Score to quantify the forecasting performance of the ensemble formed by the neural networks repetitions. We show that while the performance of the single predictor formed by the ensemble mean improves throughout the training process, the reliability of the associated confidence intervals starts to decrease shortly after the initiation of this process. While there is no moment during the training where the reliability of the confidence intervals is perfect, we show that it is best after approximately 5 to 10 iterations, depending on the basin. We also show that the Continuous Ranked Probability Score and the logarithmic score do not evolve in the same fashion during the training, due to a particularity of the logarithmic score.Correspondence to: M.-A. Boucher (marie-a.boucher.1@ulaval.ca)

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“…Radial basis function neural network (RBFNN) has several special characteristics like simple architecture and faster performance [26]. It is a special neural network with a single hidden layer.…”

Section: Radial Basis Function Neuralmentioning

confidence: 99%

“…The nonlinear basis function is a function of the normalized radial distance between the input vector and the weight vector. The most widely used form of radial basis function is the Gaussian function given by [26] …”

Section: Radial Basis Function Neuralmentioning

confidence: 99%

Artificial Neural Network Analysis of Sierpinski Gasket Fractal Antenna: A Low Cost Alternative to Experimentation

Dhaliwal

Pattnaik

2013

Advances in Artificial Neural Systems

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Artificial neural networks due to their general-purpose nature are used to solve problems in diverse fields. Artificial neural networks (ANNs) are very useful for fractal antenna analysis as the development of mathematical models of such antennas is very difficult due to complex shapes and geometries. As such empirical approach doing experiments is costly and time consuming, in this paper, application of artificial neural networks analysis is presented taking the Sierpinski gasket fractal antenna as an example. The performance of three different types of networks is evaluated and the best network for this type of applications has been proposed. The comparison of ANN results with experimental results validates that this technique is an alternative to experimental analysis. This low cost method of antenna analysis will be very useful to understand various aspects of fractal antennas.

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Suitability of different neural networks in daily flow forecasting

Cited by 83 publications

References 19 publications

An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey

An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey

An experiment on the evolution of an ensemble of neural networks for streamflow forecasting

Artificial Neural Network Analysis of Sierpinski Gasket Fractal Antenna: A Low Cost Alternative to Experimentation

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