Advances in ungauged streamflow prediction using artificial neural networks

Besaw, Lance E.; Rizzo, Donna M.; Bierman, Paul R.; Hackett, William R.

doi:10.1016/j.jhydrol.2010.02.037

Cited by 159 publications

(65 citation statements)

References 46 publications

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“…Estimating rainfall-runoff relationship and streamflow accurately is a significant element which should be considered for managing water resources effectively [1,2]. Hydrologic practices, including water supply and allocation, reservoir planning and operation, flood and drought management, and other hydrological applications, can be conducted successfully only when the rainfall-runoff relationship and streamflow behavior in a river watershed are estimated accurately.…”

Section: Introductionmentioning

confidence: 99%

“…The MLMs included artificial neural network (ANN) [1,8], neuro-fuzzy (NF) [9], support vector machines (SVMs) (for regression, also called support vector regression (SVR)) [10,11], random forest (RF) [12], least squares support vector machine (LSSVM) (for regression, also called least squares support vector regression (LSSVR)) [13,14] and extreme learning machine (ELM) [15,16]. The MLMs are able to deal with nonlinearity and non-stationarity inherent in rainfall-runoff relationship and streamflow time series effectively.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

2018

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Accurate modeling for nonlinear and nonstationary rainfall-runoff processes is essential for performing hydrologic practices effectively. This paper proposes two hybrid machine learning models (MLMs) coupled with variational mode decomposition (VMD) to enhance the accuracy for daily rainfall-runoff modeling. These hybrid MLMs consist of VMD-based extreme learning machine (VMD-ELM) and VMD-based least squares support vector regression (VMD-LSSVR). The VMD is employed to decompose original input and target time series into sub-time series called intrinsic mode functions (IMFs). The ELM and LSSVR models are selected for developing daily rainfall-runoff models utilizing the IMFs as inputs. The performances of VMD-ELM and VMD-LSSVR models are evaluated utilizing efficiency and effectiveness indices. Their performances are also compared with those of VMD-based artificial neural network (VMD-ANN), discrete wavelet transform (DWT)-based MLMs (DWT-ELM, DWT-LSSVR, and DWT-ANN) and single MLMs (ELM, LSSVR, and ANN). As a result, the VMD-based MLMs provide better accuracy compared with the single MLMs and yield slightly better performance than the DWT-based MLMs. Among all models, the VMD-ELM and VMD-LSSVR models achieve the best performance in daily rainfall-runoff modeling with respect to efficiency and effectiveness. Therefore, the VMD-ELM and VMD-LSSVR models can be an alternative tool for reliable and accurate daily rainfall-runoff modeling.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

2018

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“…Continuous streamflow records in such cases can provide systematic prediction models using time series analysis. In recent literature, due to advances in computing systems, application of artificial intelligence (AI) techniques for cross-station or singlestation daily or monthly streamflow prediction has been investigated, and successful results have been reported (Ochoa-Rivera et al 2002;Kisi and Cigizoglu 2007;Kisi 2008;Demirel et al 2009;Toprak et al 2009;Besaw et al 2010;Can et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Successive-station monthly streamflow prediction using different artificial neural network algorithms

Mehr

Kahya

ŞAHİN

et al. 2014

Int. J. Environ. Sci. Technol.

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In this study, applicability of successive-station prediction models, as a practical alternative to streamflow prediction in poor rain gauge catchments, has been investigated using monthly streamflow records of two successive stations on Ç oruh River, Turkey. For this goal, at the first stage, based on eight different successive-station prediction scenarios, feed-forward back-propagation (FFBP) neural network algorithm has been applied as a brute search tool to find out the best scenario for the river. Then, two other artificial neural network (ANN) techniques, namely generalized regression neural network (GRNN) and radial basis function (RBF) algorithms, were used to generate two new ANN models for the selected scenario. Ultimately, a comparative performance study between the different algorithms has been performed using Nash-Sutcliffe efficiency, squared correlation coefficient, and root-meansquare error measures. The results indicated a promising role of successive-station methodology in monthly streamflow prediction. Performance analysis showed that only 1-month-lagged record of both stations was satisfactory to achieve accurate models with high-efficiency value. It is also found that the RBF network resulted in higher performance than FFBP and GRNN in our study domain.

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“…Soft computing approaches such as artificial neural networks (ANNs), support vector machines (SVMs) and adaptive neuro-fuzzy inference system (ANFIS) have been widely applied for modeling complex nonlinear hydrological relationships including precipitation, streamflow, rainfall-runoff, evaporation and groundwater [1][2][3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

River Stage Modeling by Combining Maximal Overlap Discrete Wavelet Transform, Support Vector Machines and Genetic Algorithm

Seo

Choi

2017

Water

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This paper proposes a river stage modeling approach combining maximal overlap discrete wavelet transform (MODWT), support vector machines (SVMs) and genetic algorithm (GA). The MODWT decomposes original river stage time series into sub-time series (detail and approximation components). The SVM computes daily river stage values using the decomposed sub-time series. The GA searches for the optimal hyperparameters of SVM. The performance of MODWT-SVM models is evaluated using efficiency and effectiveness indices; and compared with that of a single model (multilayer perceptron (MLP) and SVM), discrete wavelet transform (DWT)-based models (DWT-MLP and DWT-SVM) and MODWT-MLP models. The conjunction of MODWT, SVM and GA improves the performance of the SVM model and outperforms the single models. The MODWT-based models using the SVM model enhance model performance and accuracy compared to those of using MLP model. Also, hybrid models coupling MODWT, SVM and GA improve model performance and accuracy in daily river stage modeling as compared with those combined with DWT. The MODWT-SVM model using the Coiflet 12 (c12) mother wavelet, MODWT-SVM-c12, produces the best efficiency and effectiveness among all models. Therefore, the conjunction of MODWT, SVM and GA can be an efficient and effective approach for modeling daily river stages.

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Advances in ungauged streamflow prediction using artificial neural networks

Cited by 159 publications

References 46 publications

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

Successive-station monthly streamflow prediction using different artificial neural network algorithms

River Stage Modeling by Combining Maximal Overlap Discrete Wavelet Transform, Support Vector Machines and Genetic Algorithm

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