Wavelet Regression Technique for Streamflow Prediction

Küçük, Murat; ̇oğlu, Necati Ağirali

doi:10.1080/02664760600744298

Cited by 77 publications

(20 citation statements)

References 21 publications

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“…Other authors have also considered the use of wavelet coefficients as predictors in other settings; for example Ramsey and Lamport (1998) constructed regression models relating the DWT of both covariate and response time series in economic data and Küçük and Agiralioglu (2006) construct similar models in streamflow modeling. Further, wavelet packets (a generalisation of wavelets which results in a much richer set of potential basis functions) have been used in modeling transient underwater signals (Learned and Willsky, 1995), sales data (Michis, 2009), wastewater filtering (Lee et al, 2009), sleep state modeling ) and wind speed prediction (Hunt and Nason, 2001;Nason and Sapatinas, 2002).…”

Section: Discussionmentioning

confidence: 99%

Classification of multiple time signals using localized frequency characteristics applied to industrial process monitoring

Aykroyd

Barber

Miller

2016

Computational Statistics & Data Analysis

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A general framework for regression modeling using localized frequency characteristics of explanatory variables is proposed. This novel framework can be used in any application where the aim is to model an evolving process sequentially based on multiple time series data. Furthermore, this framework allows time series to be transformed and combined to simultaneously boost important characteristics and reduce noise. A wavelet transform is used to isolate key frequency structure and perform data reduction. The method is highly adaptive, since wavelets are effective at extracting localized information from noisy data. This adaptivity allows rapid identification of changes in the evolving process. Finally, a regression model uses functions of the wavelet coefficients to classify the evolving process into one of a set of states which can then be used for automatic monitoring of the system. As motivation and illustration, industrial process monitoring using electrical tomography measurements is considered. This technique provides useful data without intruding into the industrial process. Statistics derived from the wavelet transform of the tomographic data can be enormously helpful in monitoring and controlling the process. The predictive power of the proposed approach is explored using real and simulated tomographic data. In both cases, the resulting models successfully classify different flow regimes and hence provide the basis for reliable online monitoring and control of

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Section: Discussionmentioning

confidence: 99%

Classification of multiple time signals using localized frequency characteristics applied to industrial process monitoring

Aykroyd

Barber

Miller

2016

Computational Statistics & Data Analysis

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“…Besides, wavelets are excellent tools for non-stationary signal analysis. There are numerous wavelet applications for the analysis of hydro-climatic variables in literature (Torrence and Compo, 1998;Smith et al, 1998;Park et al, 2000;Drago and Boxall 2002;Yan et al, 2004;Labat, 2005;Küçük and Aǧıralioǧlu, 2006;Partal and Kucuk, 2006). To the knowledge of the authors, no work has been published in the literature that addresses the application of WNN to evapotranspiration estimation.…”

Section: Introductionmentioning

confidence: 98%

Modelling evapotranspiration using discrete wavelet transform and neural networks

Partal

2009

Hydrological Processes

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Abstract:This study combines wavelet transforms and feed-forward neural network methods for reference evapotranspiration estimation. The climatic data (air temperature, solar radiation, wind speed, relative humidity) from two stations in the United States was evaluated for estimating models. For wavelet and neural network (WNN) model, the input data was decomposed into wavelet sub-time series by wavelet transformation. Later, the new series (reconstructed series) are produced by adding the available wavelet components and these reconstructed series are used as the input of the WNN model. This phase is pre-processing of raw data and the main different of the WNN model. The performance of the WNN model was compared with classical neural networks approach [artificial neural network (ANN)], multi-linear regression and Hargreaves empirical method. This study shows that the wavelet transforms and neural network methods could be applied successfully for evapotranspiration modelling from climatic data.

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“…2). The low-frequency components reflected by A3 showed the broad-scale patterns in the predictor dataset including its periodicity and trends, and was closely in-phase with the predictor signal, whereas the high-frequency components (db1, db2, db3] appeared to replicate greater details of the subtle but significant patterns in the UWD time-series [KÜÇÜK, AĞIRALI˙ OĞLU 2006].…”

Section: Elm Elm B and Elm W Model Developmentmentioning

confidence: 99%

Water demand forecasting using extreme learning machines

Tiwari¹,

Adamowski²,

Adamowski³

2016

Journal of Water and Land Development

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The capacity of recently-developed extreme learning machine (ELM) modelling approaches in forecasting daily urban water demand from limited data, alone or in concert with wavelet analysis (W) or bootstrap (B) methods (i.e., ELM, ELM W , ELM B ), was assessed, and compared to that of equivalent traditional artificial neural network-based models (i.e., ANN, ANN W , ANN B ). The urban water demand forecasting models were developed using 3-year water demand and climate datasets for the city of Calgary, Alberta, Canada. While the hybrid ELM B and ANN B models provided satisfactory 1-day lead-time forecasts of similar accuracy, the ANN W and ELM W models provided greater accuracy, with the ELM W model outperforming the ANN W model. Significant improvement in peak urban water demand prediction was only achieved with the ELM W model. The superiority of the ELM W model over both the ANN W or ANN B models demonstrated the significant role of wavelet transformation in improving the overall performance of the urban water demand model.

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Wavelet Regression Technique for Streamflow Prediction

Cited by 77 publications

References 21 publications

Classification of multiple time signals using localized frequency characteristics applied to industrial process monitoring

Classification of multiple time signals using localized frequency characteristics applied to industrial process monitoring

Modelling evapotranspiration using discrete wavelet transform and neural networks

Water demand forecasting using extreme learning machines

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