Streamflow Prediction by Applying Generalized Regression Network with Time Series Decomposition Method

Abstract

Precise and correct estimation of streamflow is important for the operative progression in water resources systems. The artificial intelligence approaches; such as artificial neural networks (ANN) have been applied for efficiently tackling the hydrological matters like streamflow forecasting in this study at upper Yangtze River. The objective is to investigate the certainty of monthly streamflow by applying artificial neural networks including Generalized Regression Network (GRNN). To overcome the non-linearity problem of streamflow, artificial neural networks integrated with discrete wavelet transform (DWT). Data has been analyzed by comparing the simulation outputs of the models with the correlation coefficient (R) root mean square errors (RMSE). It is found that the decomposition technique DWT has ability to improve the forecasting results as compare to single applied artificial neural networks. Moreover, all applied models are separately applies on the peak values as well which also have showed that intergrated model has more ability to catch the peak values

Radial basis function neural network for 2 satisfiability programming

“…RBFNN is a neural network that was first used in 1989 [2]. Radial Basis Function Neural Network algorithm has been proven to be useful and beneficial in many industrial applications [21]. RBFNN is different from other networks, possessing several distinctive features due to its universal approximation ability, more compact topology and faster learning speed [22][23].…”

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

“…The RBF is an effective ANN model that was introduced by Powell (1987) for interpolating and simulating multidimensional fields (Sahin 1997, Tayyab et al 2016. The type of connection between the hidden layer and inputs in the RBF network are not weighted and there are radially symmetric transfer functions on the hidden layer nodes (Jayawardena and Fernando 1998).…”

“…The type of connection between the hidden layer and inputs in the RBF network are not weighted and there are radially symmetric transfer functions on the hidden layer nodes (Jayawardena and Fernando 1998). There are several basic functions for training the RBF but, from the point of view of RBF performance, the type chosen is not crucial and the Gaussian function is the most common (Jayawardena and Fernando 1998, Balouchi et al 2015, Tayyab et al 2016. The output vector (y j ) from input vector (X) can be given by (Balouchi et al 2015):…”

“…The GRNN introduced by Specht (1991) is a type of ANN model (Specht 1991). This model is defined based on the RBF model and has four layers: the input layer, the RBF (or pattern) layer, the summation layer and the output layer (Tayyab et al 2016, Yin et al 2016. The Gaussian function (Equation 2) is implemented in the RBF layer.…”

“…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).…”