The Research of Monthly Discharge Predictor-corrector Model Based on Wavelet Decomposition

By Huicheng Zhou, Yong Peng, Guo-hua Liang. 2008

93 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Environmental Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/d7d781271f57912acabbaed6416e341ae5f7992a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Jan Adamowski
2. Mukesh Tiwari
3. Vahid Nourani
4. A. H. Baghanam
5. V. Sehgal
6. Akira Mano
7. M. Komasi
8. Maryam Shafaei

In Environmental Engineering

1. Jan Adamowski
2. A. H. Baghanam
3. Mukesh Tiwari
4. Vahid Nourani
5. Ozgur Kisi
6. V. Sehgal
7. Chandranath Chatterjee
8. Akira Mano

In Safety, Risk, Reliability and Quality

1. V. Sehgal
2. Akira Mano
3. Jan Adamowski
4. M. Komasi
5. Mukesh Tiwari
6. Maryam Shafaei
7. Chandranath Chatterjee
8. Vahid Nourani

Citations in the corpus

• Lake Level Forecasting Using Wavelet-SVR, Wavelet-ANFIS and Wavelet-ARMA Conjunction Models (2015, methodology): “ARMA model is constituted of autoregressive (AR) model and moving average (MA) model (Zhou et al. 2008).”
• Wavelet Regression Model as an Alternative to Neural Networks for River Stage Forecasting (2011, methodology): “In the last decade, wavelet transform has become a useful technique for analyzing variations, periodicities and trends in a time series (Labat et al. 2000, 2005 ;S mith et al. 1998 ;L u2002; Chou and Wang 2002; Xingang et al. 2003; Coulibaly and Burn 2004; Labat 2005 ;P artal and Kucuk2006; Zhou et”
• Evapotranspiration modeling using a wavelet regression model (2010, methodology): “In the last decade, wavelet transform has become a useful technique for analyzing variations, periodicities and trends in time series (Labat et al. 2000; Smith et al. 1998; Lu 2002; Chou and Wang 2002; Xingang et al. 2003; Coulibaly and Burn 2004; Labat 2005; Labat et al. 2005; Partal and Küçük 2006”
• A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling (2009, methodology): “As an example, Zhou et al. (2008) developed a wavelet predictor-corrector model for prediction of monthly discharge time series and showed that the model has higher prediction accuracy than ARIMA and seasonal ARIMA.”
• Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review (2014, methodology): “A number of recent hydrological studies have implemented wavelet analysis (e.g., Adamowski and Sun, 2010; Kim and Valdes, 2003; Kisi, 2009a,b, 2010; Nourani et al., 2009a,b, 2011; Maheswaran and Khosa, 2012a; Partal and Kisi, 2007; Sang, 2012; Tiwari and Chatterjee, 2010; Zhou et al., 2008).”
• Wavelet Bootstrap Multiple Linear Regression Based Hybrid Modeling for Daily River Discharge Forecasting (2014, methodology): “…employed in many hydrology and water resources studies (Wang and Ding 2003; Kim and Valdes 2003; Anctil and Tape 2004; Partal and Cigizoglu 2008; Zhou et al. 2008; Adamowski and Sun 2010; Adamowski et al. 2012; Kisi and Shiri 2012; Sahay and Sehgal 2013; Sahay and Srivastava 2014; Sehgal et al.…”