A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction

Ghorbani, Mohammad Ali; Zadeh, Hojat Ahmad; Isazadeh, Mohammad; Terzi, Özlem

doi:10.1007/s12665-015-5096-x

Cited by 136 publications

(57 citation statements)

References 31 publications

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“…RBF kernel function has advantages compared with other kernel functions including linear, sigmoid, and polynomial kernel functions in terms of nonlinear mapping capability, parameter number, numerical limiting conditions, global superiority, and positive definite [73]. Furthermore, linear kernel function is effective only for linear problems, sigmoid kernel function is not applied widely, and polynomial kernel function suffers from computational difficulties [74]. On the other hand, RBF kernel function can be used for any problems as long as the parameter is selected appropriately [75].…”

Section: Least Squares Support Vector Regression (Lssvr)mentioning

confidence: 99%

“…Furthermore, linear kernel function is effective only for linear problems, sigmoid kernel function is not applied widely, and polynomial kernel function suffers from computational difficulties [74]. On the other hand, RBF kernel function can be used for any problems as long as the parameter is selected appropriately [75].…”

Section: Artificial Neural Network (Ann)mentioning

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: Least Squares Support Vector Regression (Lssvr)mentioning

confidence: 99%

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

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

2018

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“…Dorum et al (2010) studied to set up rainfall-runoff 1 3 85 Page 2 of 9 relationship using ANN and ANFIS models at hydrometric stations on seven sites in Susurluk watershed. Ghorbani et al (2016) investigated the applicability of MLP, RBF and SVM models for the estimation of river flow. The results show that the RBF and MLP models are better for estimation monthly river flow.…”

Section: Introductionmentioning

confidence: 99%

River flow simulation using a multilayer perceptron-firefly algorithm model

Darbandi

Pourhosseini

2018

Appl Water Sci

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River flow estimation using records of past time series is importance in water resources engineering and management and is required in hydrologic studies. In the past two decades, the approaches based on the artificial neural networks (ANN) were developed. River flow modeling is a non-linear process and highly affected by the inputs to the modeling. In this study, the best input combination of the models was identified using the Gamma test then MLP-ANN and hybrid multilayer perceptron (MLP-FFA) is used to forecast monthly river flow for a set of time intervals using observed data. The measurements from three gauge at Ajichay watershed, East Azerbaijani, were used to train and test the models approach for the period from January 2004 to July 2016. Calibration and validation were performed within the same period for MLP-ANN and MLP-FFA models after the preparation of the required data. Statistics, the root mean square error and determination coefficient, are used to verify outputs from MLP-ANN to MLP-FFA models. The results show that MLP-FFA model is satisfactory for monthly river flow simulation in study area.

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“…By plotting the 95% CB using the HBMES-1 method introduced in Section 2.4.1, the uncertainty statistics for the 4 entropy models and the global shear stress model introduced in Section 2.2 were obtained and presented in Table 2. According to the researchers' studies, the predictor model is highly reliable if 80-100% of the values are in the desired CB and the model was not able to predict if less than 50% of the values are within the CB [30,32,49,50].…”

Section: Assessment Of Uncertainty Of Four Entropy Models Using the Hmentioning

confidence: 99%

“…The uncertainty analysis of many hydraulic and hydrological models has been tackled by many researchers using various uncertainty methods [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40]. Lamb et al [41] used measured water depth in a Bayesian procedure as Generalized Likelihood Uncertainty Estimation (GLUE) to measure uncertainties for a simple model of the rainfall-runoff distribution.…”

mentioning

confidence: 99%

A Bayesian Monte-Carlo Uncertainty Model for Assessment of Shear Stress Entropy

Kazemian-Kale-Kale¹,

Gholami²,

Rezaie-Balf³

et al. 2020

Preprint

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The entropy models have been recently adopted in many studies to evaluate the distribution of the shear stress in circular channels. However, the uncertainty in their predictions and their reliability remains an open question. We present a novel method to evaluate the uncertainty of four popular entropy models, including Shannon, Shannon-Power Low (PL), Tsallis, and Renyi, in shear stress estimation in circular channels. The Bayesian Monte-Carlo (BMC) uncertainty method is simplified considering a 95% Confidence Bound (CB). We developed a new statistic index called as FREEopt-based OCB (FOCB) using the statistical indices Forecasting Range of Error Estimation (FREE) and the percentage of observed data in the CB (Nin), which integrates their combined effect. The Shannon and Shannon PL entropies had close values of the FOCB equal to 8.781 and 9.808, respectively, had the highest certainty in the calculation of shear stress values in circular channels followed by traditional uniform flow shear stress and Tsallis models with close values of 14.491 and 14.895, respectively. However, Renyi entropy with much higher values of FOCB equal to 57.726 has less certainty in the estimation of shear stress than other models. Using the presented results in this study, the amount of confidence in entropy methods in the calculation of shear stress to design and implement different types of open channels and their stability is determined.

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A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction

Cited by 136 publications

References 31 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

River flow simulation using a multilayer perceptron-firefly algorithm model

A Bayesian Monte-Carlo Uncertainty Model for Assessment of Shear Stress Entropy

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