Prediction of daily suspended sediment load using wavelet and neurofuzzy combined model

Rajaee, Taher; Mirbagheri, Seyed Ahmad; Nourani, Vahid; Alikhani, A.

doi:10.1007/bf03326121

Cited by 60 publications

(32 citation statements)

References 48 publications

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“…Some studies have used all of their wavelet sub-series [ADA- MOWSKI, SUN 2010;NOURANI et al 2009;WANG, DING 2003] whereas others have removed the db1 sub-series and added the remaining series, considering the former series as noise due to its low correlation with their original data [KISI, CIMEN 2011;PARTAL, KIŞI 2007;RAJAEE et al 2010]. However, in some studies, new wavelet time-series were developed by adding up the effective DWCs based on regression correlation [TIWARI, CHATTERJEE 2010b;2011].…”

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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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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“…This supplies a time frequency representation of a signal at many different periods in the time domain [21]. Wavelet model transforms the data from original time series to improve the ability of predicting by capturing useful information on various resolution levels [22], [23]. Wavelet decomposition is the one, which decomposes time series data into a different time and scale of wavelet transformation, and thus one can get the property of time series in different frequency bands as time goes by [24].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Air Temperature using ANFIS by Wavelet Refined Parameters

S¹,

Deka²

2016

IJISA

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Abstract-The precise modeling of average air temperature is a significant and much essential parameter in frame of reference for decision-making in agriculture field, drought detection and environmental related issues. The aim of this research is to construct an accurate model to modeling average air temperature using hybrid Wavelet-ANFIS techniques. Being cognizant of the fact, uncertainty handling capability is achieved with ANFIS technique; a cognitive approach to integrate ANFIS technique along with pre-processed data by using Wavelet transformation. Detailing on approach, in this work utilized Discrete Wavelet transform under Daubechies mother Wavelet up to 3rd level of decomposition. This study extends up to seven station's meteorological data records. The following developed hybrid model's performance is compared with single ANFIS models for all seven stations. The obtained results were evaluated using correlation coefficient, root mean square error and scatter index These results confirmed that the proposed hybridized Wavelet-ANFIS model has estimable potential in terms of modeling temperature than ANFIS model alone.

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“…However, conventional spectrophotometric methods yield poor results for quantitative analysis of multi-component mixtures due to overlapping absorption spectra in the same spectral region. Therefore, the representation of a signal by means of its spectrum or Fourier transform is essential for solving mentioned problems in different science (Nounou and Nounou 2010;Rajaee et al 2010;Jalalkamali et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Simultaneous spectrophotometric determination of phosphate and silicate in different water and soil samples using chemometrics approach

Tarighat

2015

Int. J. Environ. Sci. Technol.

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Continuous wavelet transform together with zero-crossing technique was applied to the overlapping visible spectra, as a very simple and accurate method, for the simultaneous determination of phosphate and silicate without any prior separation steps. Daubechies-4, Symlet 8, and Meyer continuous wavelet transforms for signal treatments were found to be suitable. The calibration graphs for estimation of complexes were obtained by measuring the wavelet coefficients at zero-crossing points for silicomolybdenum blue and phosphomolybdenum blue.The constructed calibration functions were tested by analyzing the composition of the different binary mixtures. The experimental data were also analyzed using partial least squares and first-order derivative spectrophotometric method. The proposed method was validated by simultaneous determination of phosphate and silicate in different environmental waters and soil samples. The results of proposed method have been compared with those predicted by partial least squares and standard methods.

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Prediction of daily suspended sediment load using wavelet and neurofuzzy combined model

Cited by 60 publications

References 48 publications

Water demand forecasting using extreme learning machines

Water demand forecasting using extreme learning machines

Modeling of Air Temperature using ANFIS by Wavelet Refined Parameters

Simultaneous spectrophotometric determination of phosphate and silicate in different water and soil samples using chemometrics approach

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