Artificial neural networks as a tool in urban storm drainage

Loke, E.; Warnaars, Eric; Jacobsen, Peter; Nelen, F.; Almeida, Maria do Céu

doi:10.2166/wst.1997.0651

Cited by 24 publications

(15 citation statements)

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“…In the last decade, ANNs have been successfully employed in modeling a wide range of hydrologic processes, including rainfall-runoff processes; Smith and Eli (1995); Hsu et al (1995); Minns and Hall (1996); Shamseldin (1997); Dawson and Wilby (1998); Cigizoglu and Alp (2004) studied on neural-network models of rainfall-runoff process, Mason et al (1996) used radial basis function (RBF) ANN for rainfall-runoff modeling, Shamseldin et al (1997) have presented methods for combining the outputs of the different rainfall-runoff models, Loke et al (1997) studied the application of an ANN for prediction of runoff coefficient by the use of simple catchment data. Fernando and Jayawardena (1998) studied on runoff forecasting using RBF networks with OLS algorithm, Tokar and Johnson (1999) developed an ANN model to predict daily runoff as a function of daily precipitation, temperature, and snowmelt for a watershed in Maryland, USA.…”

Section: Introductionmentioning

confidence: 99%

An application of artificial intelligence for rainfall-runoff modeling

Aytek

Asce

Алп³

2008

J Earth Syst Sci

106

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This study proposes an application of two techniques of artificial intelligence (AI) for rainfall-runoff modeling: the artificial neural networks (ANN) and the evolutionary computation (EC). Two different ANN techniques, the feed forward back propagation (FFBP) and generalized regression neural network (GRNN) methods are compared with one EC method, Gene Expression Programming (GEP) which is a new evolutionary algorithm that evolves computer programs. The daily hydrometeorological data of three rainfall stations and one streamflow station for Juniata River Basin in Pennsylvania state of USA are taken into consideration in the model development. Statistical parameters such as average, standard deviation, coefficient of variation, skewness, minimum and maximum values, as well as criteria such as mean square error (MSE) and determination coefficient (R 2 ) are used to measure the performance of the models. The results indicate that the proposed genetic programming (GP) formulation performs quite well compared to results obtained by ANNs and is quite practical for use. It is concluded from the results that GEP can be proposed as an alternative to ANN models.

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Section: Introductionmentioning

confidence: 99%

An application of artificial intelligence for rainfall-runoff modeling

Aytek

Asce

Алп³

2008

J Earth Syst Sci

106

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“…Four neural solutions were developed on identical input datasets but the output is different: it is a calculated "runoff coefficient". Loke et al (1997) modelled this dimensionless empirical parameter with a NN. It describes the proportion of total rainfall in a storm event that is converted into runoff.…”

Section: Introductionmentioning

confidence: 99%

Neural network modelling of non-linear hydrological relationships

Abrahart

See

2007

Hydrol. Earth Syst. Sci.

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Abstract. Two recent studies have suggested that neural network modelling offers no worthwhile improvements in comparison to the application of weighted linear transfer functions for capturing the non-linear nature of hydrological relationships. The potential of an artificial neural network to perform simple non-linear hydrological transformations under controlled conditions is examined in this paper. Eight neural network models were developed: four full or partial emulations of a recognised non-linear hydrological rainfallrunoff model; four solutions developed on an identical set of inputs and a calculated runoff coefficient output. The use of different input combinations enabled the competencies of solutions developed on a reduced number of parameters to be assessed. The selected hydrological model had a limited number of inputs and contained no temporal component. The modelling process was based on a set of random inputs that had a uniform distribution and spanned a modest range of possibilities. The initial cloning operations permitted a direct comparison to be performed with the equation-based relationship. It also provided more general information about the power of a neural network to replicate mathematical equations and model modest non-linear relationships. The second group of experiments explored a different relationship that is of hydrological interest; the target surface contained a stronger set of non-linear properties and was more challenging. Linear modelling comparisons were performed against traditional least squares multiple linear regression solutions developed on identical datasets. The reported results demonstrate that neural networks are capable of modelling nonlinear hydrological processes and are therefore appropriate tools for hydrological modelling.

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“…Such a model resembles the brain in two aspects: (1) knowledge is acquired by the neurons through a learning process and (2) inter-neuron connection strengths, known as synaptic weights, are used to store the knowledge (Haykin 1994). The learning process encompasses the adjustment of the weights of each processing unit, and can be seen as teaching the network to yield a particular response to a specific input (Loke 1999). Learning is accomplished by using a so called "training algorithm", that is a mathematical formulation of the rules that determine the magnitude of weight adjustment.…”

Section: The Ann Modelmentioning

confidence: 99%

Model Reduction Using Neural Networks Applied To The Modeling Of Integrated Urban Wastewater Systems

Ráduly¹,

Capodaglio²,

Lindblom³

et al. 2006

ECMS 2006 Proceedings Edited By: W. Borutzky, A. Orsoni, R. Zobel

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Simulation of the integrated urban wastewater system is a computationally-demanding task, and performing long-term simulations consequently takes a considerable time. Reduction of simulation times can be achieved by speeding up one or more of the submodels of the integrated system. In this paper the use of a fast neural network model instead of the mechanistic model of the wastewater treatment plant (WWTP) is proposed for this purpose. The neural network is trained on a sequence of treatment plant input/output data generated by the mechanistic model of the WWTP, i.e. it is a reduced model of the original WWTP model. As a result of model substitution a reduction of the simulation time by a factor of 23 was achieved. The results presented in this paper show that the errors introduced by the WWTP model substitution are of an acceptable level, confirming the practical usefulness of the proposed method.

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Artificial neural networks as a tool in urban storm drainage

Cited by 24 publications

References 0 publications

An application of artificial intelligence for rainfall-runoff modeling

An application of artificial intelligence for rainfall-runoff modeling

Neural network modelling of non-linear hydrological relationships

Model Reduction Using Neural Networks Applied To The Modeling Of Integrated Urban Wastewater Systems

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