A Comparative Assessment of Artificial Neural Network, Generalized Regression Neural Network, Least-Square Support Vector Regression, and K-Nearest Neighbor Regression for Monthly Streamflow Forecasting in Linear and Nonlinear Conditions

Modaresi, Fereshteh; Araghinejad, Shahab; Ebrahimi, Kumars

doi:10.1007/s11269-017-1807-2

Cited by 101 publications

(35 citation statements)

References 23 publications

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“…The second statement of Equation ( 6) is related to the penalty function of the difference between the actual output values and the model. Regarding the parameter C, it can be said that small or large values of this parameter in the statement of Equation ( 6) cause the simplicity or complexity of the model (Modaresi et al, 2018).…”

Section: Least-squares Support Vector Machine (Lssvm)mentioning

confidence: 99%

Using soft computing and machine learning algorithms to predict the discharge coefficient of curved labyrinth overflows

Karami

Rezaei

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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This research aims to estimate the overflow capacity of a curved labyrinth using different intelligent prediction models, namely the adaptive neural-fuzzy inference system, the support vector machine, the M5 model tree, the least-squares support vector machine and the least-squares support vector machine-bat algorithm (LSSVM-BA). A total of 355 empirical data for 6 different congressional overflow models were extracted from the results of a laboratory study on labyrinth overflow models. The parameters of the upstream water head to overflow ratio, the lateral wall angle and the curvature angle were used to estimate the discharge coefficient of curved labyrinth overflows. Based on various statistical evaluation indicators, the results show that those input parameters can be relied upon to predict the discharge coefficient. Specifically, the LSSVM-BA model showed the best prediction accuracy during the training and test phases. Such a low-cost prediction model may have a remarkable practical implication as it could be an economic alternative to the expensive laboratory solution, which is costly and time-consuming.

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Section: Least-squares Support Vector Machine (Lssvm)mentioning

confidence: 99%

Using soft computing and machine learning algorithms to predict the discharge coefficient of curved labyrinth overflows

Karami

Rezaei

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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“…Another method that is being used to maximize available data is the “cross‐validation” technique. This method involves using a distinct test set to evaluate the model performance at different learning phases (Modaresi, Araghinejad, & Ebrahimi, ). In the cross‐validation method, the norm is to split the available data into three: training, testing, and validation sets (Kisi, Mansouri, & Hu, ).…”

Section: Model Development Processesmentioning

confidence: 99%

Neural network modeling of hydrological systems: A review of implementation techniques

Oyebode

Stretch

2018

Natural Resource Modeling

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This paper presents a review of procedural steps and implementation techniques used in the development of artificial intelligence models, generally referred to as artificial neural networks (ANNs), within the water resources domain. It focusses on identifying different areas wherein ANNs have found application thereby elucidating its advantages and disadvantages as well as various challenges encountered in its use. Results from this review provide useful insights into how the performance of ANNs can be improved and potential areas of application that are yet to be explored in hydrological modeling. Recommendations for Resource Managers • Development of integrated and hybrid artificial intelligent tools is critical to achieving improved forecasts in hydrological modeling studies. • Further research into comprehending the internal mechanisms of neural networks is required to obtain a practical meaning of each network component deployed to solve real-world problems.• More robust optimization techniques and tools like differential evolution, particle swarm optimization and deep neural nets, are yet to be fully explored in the water resources analysis, and should be given more attention to enhance neural networks aptitude for modeling complex and nonlinear hydrological processes.Natural Resource Modeling. 2019;32:e12189.wileyonlinelibrary.com/journal/nrm

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“…The amount of spread in this model is in the range of > 0, and should be determined by the user by the trial-anderror method; the typical amount is 1.0. The number of neurons in the RBF layer is equal to the number of input data; in the summation layer it is two neurons, the Dsummation neuron and the S-summation neuron (Modaresi et al 2017). More details and applications of the GRNN model are presented in Specht (1991), Yin et al (2016), Tayyab et al (2016) and Modaresi et al (2017).…”

Section: General Regression Neural Network (Grnn)mentioning

confidence: 99%

“…In the past two decades, ANN models have been widely employed to predict hydrological variables such as daily, weekly and monthly runoff (Rajurkar et al 2004, Siou et al 2012, Nigam et al 2014, Nanda et al 2016. Different types of ANN models, such as multi-layer perceptron (MLP) (Jain et al 2004, Riadet al 2004, Srinivasulu and Jain 2006, Rezaeian Zadeh et al 2010, Dhamge et al 2012, Rezaeianzadeh et al 2013, Kumar et al 2015, multi-layer back-propagation ANN (BPANN) (Agarwal and Singh 2004), back-propagation neural network (BPNN), radial basis function neural network (RBF) (Jayawardena and Fernando 1998, Lin and Chen 2004, Senthil Kumar et al 2005, Lee et al 2010, Dar 2017, feedforward back-propagation (FFBP) (Shiau and Hsu 2016), general regression neural network (GRNN) (Islam et al 2001, Cigizoglu and Alp 2004, Aytek and Alp 2008, Gowda and Mayya 2014, Mishra et al 2014, Tayyab et al 2016, Modaresi et al 2017 and rotated general regression neural network (RGRNN) (Irfan et al 2016, Yin et al 2016, have been applied for rainfall-runoff simulation. In recent years, dynamic ANN models have been suggested which are more efficient than static ANN models to obtain time series (Guzman et al 2017).…”

Section: Introductionmentioning

confidence: 99%

A fusion-based neural network methodology for monthly reservoir inflow prediction using MODIS products

Ghazali

Honar

Nikoo

2018

Hydrological Sciences Journal

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A model fusion approach was developed based on five artificial neural networks (ANNs) and MODIS products. Static and dynamic ANNsthe multi-layer perceptron (MLP) with one and two hidden layers, general regression neural network (GRNN), radial basis function (RBF) and nonlinear autoregressive network with exogenous inputs (NARX)were used to predict the monthly reservoir inflow in Mollasadra Dam, Fars Province, Iran. Leaf area index and snow cover from MODIS, and rainfall and runoff data were used to identify eight different combinations to train the models. Statistical error indices and the Borda count method were used to verify and rank the identified combinations. The best results for individual ANNs were combined with MODIS products in a fusion model. The results show that using MODIS products increased the accuracy of predictions, with the MLP with two hidden layers giving the best performance. Also, the fusion model was found to be superior to the best individual ANNs.

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A Comparative Assessment of Artificial Neural Network, Generalized Regression Neural Network, Least-Square Support Vector Regression, and K-Nearest Neighbor Regression for Monthly Streamflow Forecasting in Linear and Nonlinear Conditions

Cited by 101 publications

References 23 publications

Using soft computing and machine learning algorithms to predict the discharge coefficient of curved labyrinth overflows

Using soft computing and machine learning algorithms to predict the discharge coefficient of curved labyrinth overflows

Neural network modeling of hydrological systems: A review of implementation techniques

A fusion-based neural network methodology for monthly reservoir inflow prediction using MODIS products

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