Wavelet Network Model and Its Application to the Prediction of Hydrology

By Wensheng Wang, Jing Ding. 2003

298 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1cb5bf0f83575ebdf50cea6eee82fdc81e4e6d29

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Citations in the corpus

A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling (2009, background): “In hydrology, Wang and Ding (2003) applied wavelet-network model to forecast shallow groundwater level and daily discharge.”
Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model (2017, result): “Some studies have used all of their wavelet sub-series (Adamowski and Sun 2010; Nourani et al. 2009; Wang and Ding 2003) whereas others have removed the db1 sub-series and added the remaining series, considering the former series as noise due to low correlation with their original data (Partal and…”
Wavelet Transform Method for Synthetic Generation of Daily Streamflow (2011, methodology): “ Wang and Ding (2003) presented a wavelet neural network hybrid model for predicting groundwater level and daily discharge.”
A wavelet‐based multiple linear regression model for forecasting monthly rainfall (2014, background): “In Section 4, the proposed method is applied to monthly rainfall forecasting at 46 rainfall stations across South Australia and compared with a linear regression model based on data without WT.”
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 (2012, background): “Each time subseries (DW) plays a distinct role in the original times series and has different effects on the original urban water demand series [ Wang and Ding , 2003].”
Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review (2014, background): “…processes, they have been also successfully applied to model other hydro-climatologic processes (i.e., shallow watertable depths, drought, snowmelt, evapotranspiration, etc.) Wang and Ding (2003) proposed hybrid WANN models to predict monthly mean water table depths and daily discharge.”
Drought forecasting through statistical models using standardised precipitation index: a systematic review and meta-regression analysis (2019, methodology): “2009; Wang and Ding 2003).”
Wavelet Bootstrap Multiple Linear Regression Based Hybrid Modeling for Daily River Discharge Forecasting (2014, methodology): “The wavelet based hybrid models have been successfully 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…”
Lake Level Forecasting Using Wavelet-SVR, Wavelet-ANFIS and Wavelet-ARMA Conjunction Models (2015, methodology): “When either a or j is large, the frequency resolution increases, but the time domain diminishes (Wang and Ding 2003).”
A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation (2009, background): “This is in agreeing with other researches which offer the following formula to determine the decomposition level (Aussem et al., 1998; Wang and Ding, 2003): L ¼ int½log ðNÞ (11) in which L and N are decomposition level and time series length, respectively.”