A new wavelet–bootstrap–ANN hybrid model for daily discharge forecasting

Tiwari, Mukesh; Chatterjee, Chandranath

doi:10.2166/hydro.2010.142

Cited by 130 publications

(37 citation statements)

References 81 publications

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“…The bootstrap method is a simple algorithm which can be used to account for uncertainty. The combination of WA‐bootstrap‐ANN has been successfully used in other areas of hydrological forecasting [ Tiwari and Chatterjee , 2010a, 2010b], and the possible use of this method in water demand forecasting appears promising and will be studied further by the authors. In addition, there is little research available on the MNLR method in water demand forecasting.…”

Section: Resultsmentioning

confidence: 99%

Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada

Adamowski

Chan

Prasher

et al. 2012

Water Resources Research

418

165

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[1] Daily water demand forecasts are an important component of cost-effective and sustainable management and optimization of urban water supply systems. In this study, a method based on coupling discrete wavelet transforms (WA) and artificial neural networks (ANNs) for urban water demand forecasting applications is proposed and tested. Multiple linear regression (MLR), multiple nonlinear regression (MNLR), autoregressive integrated moving average (ARIMA), ANN and WA-ANN models for urban water demand forecasting at lead times of one day for the summer months (May to August) were developed, and their relative performance was compared using the coefficient of determination, root mean square error, relative root mean square error, and efficiency index. The key variables used to develop and validate the models were daily total precipitation, daily maximum temperature, and daily water demand data from 2001 to 2009 in the city of Montreal, Canada. The WA-ANN models were found to provide more accurate urban water demand forecasts than the MLR, MNLR, ARIMA, and ANN models. The results of this study indicate that coupled wavelet-neural network models are a potentially promising new method of urban water demand forecasting that merit further study.

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Section: Resultsmentioning

confidence: 99%

Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada

Adamowski

Chan

Prasher

et al. 2012

Water Resources Research

418

165

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show abstract

“…The results showed that the bagged SVM model outperforms the prediction ability of bagged multiple linear regressions (MLRs), simple SVM, and simple MLR models in all of the adopted evaluation scores. Tiwari and Chatterjee (2011) investigated hybrid wavelet bootstrapped artificial neural networks models in daily discharge forecasting. The results revealed that the model which uses the capabilities of both bootstrap and wavelet methods was the best model.…”

Section: Introductionmentioning

confidence: 99%

Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms

Erdal¹,

Karakurt

2013

Journal of Hydrology

171

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“…Because of the simplicity of W 1 (t), W 2 (t) …, W l (t), the relevant characteristics in the hydrologic dataset (e.g. periods, hidden periods, dependence and jumps) can be diagnosed through these discrete wavelet components [TIWARI, CHATTERJEE 2011]. Consequently, the prediction accuracy of drought models are improved.…”

Section: Discrete Wavelet Transformationmentioning

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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A new wavelet–bootstrap–ANN hybrid model for daily discharge forecasting

Cited by 130 publications

References 81 publications

Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada

Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada

Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms

Water demand forecasting using extreme learning machines

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