Precipitation-Runoff Modeling Using Artificial Neural Networks and Conceptual Models

Tokar, A. Sezin; Markus, Momcilo

doi:10.1061/(asce)1084-0699(2000)5:2(156)

Cited by 290 publications

(129 citation statements)

References 9 publications

Supporting

Mentioning

125

Contrasting

Unclassified

Order By: Relevance

“…For example, the Stanford Watershed Model is defined by 20-30 parameters. Optimization of model parameters is usually done by a trial and error procedure because of their large number and their complex interaction (Tokar and Johnson 1999;Tokar and Markus 2000).…”

Section: Introductionmentioning

confidence: 99%

Assessment of a conceptual hydrological model and artificial neural networks for daily outflows forecasting

Rezaeianzadeh

Stein

Tabari

et al. 2013

Int. J. Environ. Sci. Technol.

View full text Add to dashboard Cite

Artificial neural networks (ANNs) are used by hydrologists and engineers to forecast flows at the outlet of a watershed. They are employed in particular where hydrological data are limited. Despite these developments, practitioners still prefer conventional hydrological models. This study applied the standard conceptual HEC-HMS's soil moisture accounting (SMA) algorithm and the multi layer perceptron (MLP) for forecasting daily outflows at the outlet of Khosrow Shirin watershed in Iran. The MLP [optimized with the scaled conjugate gradient] used the logistic and tangent sigmoid activation functions resulting into 12 ANNs. The R 2 and RMSE values for the best trained MPLs using the tangent and logistic sigmoid transfer function were 0.87, 1.875 m 3 s -1 and 0.81, 2.297 m 3 s -1 , respectively. The results showed that MLPs optimized with the tangent sigmoid predicted peak flows and annual flood volumes more accurately than the HEC-HMS model with the SMA algorithm, with R 2 and RMSE values equal to 0.87, 0.84 and 1.875 and 2.1 m 3 s -1 , respectively. Also, an MLP is easier to develop due to using a simple trial and error procedure. Practitioners of hydrologic modeling and flood flow forecasting may consider this study as an example of the capability of the ANN for real world flow forecasting.

show abstract

Section: Introductionmentioning

confidence: 99%

Assessment of a conceptual hydrological model and artificial neural networks for daily outflows forecasting

Rezaeianzadeh

Stein

Tabari

et al. 2013

Int. J. Environ. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Figure 1 shows the architecture of ANN that consists of input layer, hidden layer, and output layer [10].…”

Section: Methodsmentioning

confidence: 99%

Comparison of Artificial Neural Network and Regression Approaches for Evaluation of Hydrodynamic Performance of Quarter Circular Break Water

Vivekanandan¹

2018

Int J Adv Tech

View full text Add to dashboard Cite

“…Figure 1 shows the architecture of ANN that consists of input layer, hidden layer, and output layer [5]. In turn, these layers have a certain number of neurons or units, so the units are also called input units, hidden units and output units.…”

Section: B) Multi-layer Perceptron Networkmentioning

confidence: 99%

Evaluation of Performance of Statistical and ANN Approaches for Prediction of Rainfall

Vivekanandan¹

2017

IJIRCST

View full text Add to dashboard Cite

Abstract-Prediction of rainfall for a river basin is of utmost importance for planning and design of irrigation and drainage systems as also for command area development. Since the distribution of rainfall varies over space and time, it is required to analyze the data covering long periods and recorded at various locations to arrive at reliable information for decision support. Further, such data need to be analyzed in different ways, depending on the issue under consideration. In the present study, Extreme Value Type-1 (EV1) distribution based on statistical approach and Multi Layer Perceptron (MLP) network based on Artificial Neural Network (ANN) is adopted for prediction of rainfall at Fatehabad and Hansi. The performance of the statistical and ANN approaches used in rainfall predication are evaluated by model performance indicators viz., correlation coefficient, model efficiency and mean absolute percentage error. The study shows the MLP is found to be better suited network for prediction of rainfall at Fatehabad whereas EV1 for Hansi. Index Terms-Artificial I. INTRODUCTIONPrediction of rainfall for a river basin is of utmost importance for planning and design of irrigation and drainage systems as also for command area development. Since the distribution of rainfall varies over space and time, it is required to analyze the data covering long periods and recorded at various locations to arrive at reliable information for decision support [1]. Further, such data need to be analyzed in different ways, depending on the issue under consideration. Out of a number of probability distributions, the family of Extreme Value Distributions (EVDs) includes Generalized Extreme Value, Extreme Value Type-1 (EV1), Extreme Value Type-2 and Extreme Value Type-3 is generally used for rainfall prediction. EVDs arise as limiting distributions for the sample of independent, identically distributed random variables, as the sample size increases. In the group of EVDs, EV1 distribution has no shape parameter as when compared to other distributions and this means that there is no change in the shape of Probability Distribution Function (PDF). Moreover, EV1 distribution has the advantage of having only positive values, since the data series of rainfall are always positive; and therefore EV1 distribution is important in statistics. Apart from this, with the development of Artificial Intelligence (AI), a number of various AI methods have been developed for prediction of rainfall. The new developed methods include Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System, Fuzzy Logic, Support Vector Machine, Neuro-Fuzzy Network and Evolutionary Optimization Algorithm. Out of these methods, ANN could deal with non-linear and complex problems in terms of classification or forecasting. The ANN models can represent a complex nonlinear relationship and extract the dependence between variables through the training process [2][3]. In the present study, statistical and ANN approaches are adopted for prediction of rainfall for the da...

show abstract

Precipitation-Runoff Modeling Using Artificial Neural Networks and Conceptual Models

Cited by 290 publications

References 9 publications

Assessment of a conceptual hydrological model and artificial neural networks for daily outflows forecasting

Assessment of a conceptual hydrological model and artificial neural networks for daily outflows forecasting

Comparison of Artificial Neural Network and Regression Approaches for Evaluation of Hydrodynamic Performance of Quarter Circular Break Water

Evaluation of Performance of Statistical and ANN Approaches for Prediction of Rainfall

Contact Info

Product

Resources

About