Simulating and predicting river discharge time series using a wavelet‐neural network hybrid modelling approach

Abstract

Abstract:Accurate simulation and prediction of the dynamic behaviour of a river discharge over any time interval is essential for good watershed management. It is difficult to capture the high-frequency characteristics of a river discharge using traditional time series linear and nonlinear model approaches. Therefore, this study developed a wavelet-neural network (WNN) hybrid modelling approach for the predication of river discharge using monthly time series data. A discrete wavelet multiresolution method was employed to decompose the time series data of river discharge into sub-series with low (approximation) and high (details) frequency, and these sub-series were then used as input data for the artificial neural network (ANN). WNN models with different wavelet decomposition levels were employed to predict river discharge 48 months ahead of time. Comparison of results from the WNN models with those of the ANN models alone indicated that WNN models performed a more accurate prediction.

Are hybrid models integrated with data preprocessing techniques suitable for monthly streamflow forecasting? Some experiment evidences

“…However, in many studies, such as Huang et al (2014), Partal and Cigizoglu (2008), Santos and Silva (2014) and Wei et al (2012), all data are decomposed into components first and later those components are divided into calibration and validation datasets to establish models. This formulation sends some amount of future information into the modeling system (Karthikeyan and Kumar, 2013;Napolitano et al, 2011;Peng, 2007); that is, the resulting components used to forecast the value of a particular moment are computed using information from future values, which would not be available at that particular moment in a forecasting exercise.…”

Modeling of Air Temperature using ANFIS by Wavelet Refined Parameters

“…The Discrete Wavelet Transform (DWT), which is based on sub-band coding, is found to be best for computation of Wavelet Transform [30]. Implementation of this method is easy and works better in terms of computation time and resources required.…”

“…Implementation of this method is easy and works better in terms of computation time and resources required. DWT of f (t) can be written as (Eq.3); The most frequent choice of the parameters a0 and b0 is 2 and 1 time steps, respectively [30]. This power of two logarithmic scaling of the time and scale is known as a dyadic grid arrangement and is the simplest and the most efficient case for practical purposes [31].…”

New fuzzy neural network–Markov model and application in mid- to long-term runoff forecast

“…A variety of methods have been developed to improve the accuracy of the mid-to long-term runoff prediction, including physical models, time series methods, conceptual models, ANN models, and many hybrid models. Now, models integrating two or more of these approaches have been proposed as predictors to improve the accuracy of mid-to long-term runoff forecasts, such as multiple regression-ANN models (Elshorbagy et al 2000), fuzzy pattern recognition models (Xiong et al 2001), the wavelet-ANN model (Anctil et al 2004, Adamowski et al 2012, Wei et al 2012, the wavelet-ANFIS hybrid model (Nourani et al 2013, Moosavi et al 2013, the wavelet-neuron fuzzy model (Partal et al 2007, Shiri et al 2010, the fuzzy-SVM model (Guo et al 2010, Hu et al 2012, the support vector regression(SVR) model (Lin et al 2006, Hong et al 2007, Wu et al 2008, Behzad et al 2009) and the wavelet-regression model (Kişi et al 2009, Kisi et al 2011 ). These hybrid models have shown different advantages for accurate predictions due to their capabilities of utilizing present information effectively.…”