Using discrete wavelet transforms to analyze trends in streamflow and precipitation in Quebec and Ontario (1954–2008)
By D. Nalley, Jan Adamowski, B. Khalil. 2012
261 citations recorded by the source corpus.
Subfields: Environmental Engineering, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/3f4e1eef59ee0a3bc796e89e75251bc44a632f1f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Environmental Engineering
- Ravinesh C. Deo
- Jan Adamowski
- John M. Quilty
- Mukesh Tiwari
- A. H. Baghanam
- Ozgur Kisi
- Vahid Nourani
- V. Sehgal
In Global and Planetary Change
- Ravinesh C. Deo
- Ozgur Kisi
- Jan Adamowski
- Mukesh Tiwari
- John M. Quilty
- A. H. Baghanam
- Vahid Nourani
- V. Sehgal
Citations in the corpus
- Wavelet Bootstrap Multiple Linear Regression Based Hybrid Modeling for Daily River Discharge Forecasting (2014, methodology): “Wavelet analysis has been largely applied for analyzing variations, periodicities and trends in time series (Nalley et al. 2012).”
- Analysis and prediction of rainfall trends over Bangladesh using Mann–Kendall, Spearman’s rho tests and ARIMA model (2017, methodology): “Nalley et al. (2012) applied modified Mann–Kendall method to avoid serial correlation problem in the hydrological time series.”
- Trend analysis using discrete wavelet transform (DWT) for long-term precipitation (1851–2006) over India (2017, methodology): “The criterion used to determine the smooth mother wavelet and the extension mode of db wavelets for the analysis was the mean relative error (MRE) (de Artigas et al. 2006, Nalley et al. 2012, Joshi et al. 2016)).”
- Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review (2014, background): “…diagram of hybrid hydrological simulations have led to the conclusion that to determine the ideal mother wavelet for a given problem a variety of mother wavelets should be tested through a trial and error process (Maheswaran and Khosa, 2012a; Nalley et al., 2012; Nourani et al., 2011; Sang, 2012).”
- Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model (2017, methodology): “Consequently, wavelet transformation can be used as ancillary tools for analysing stochastic variations, periodicity and trends in the hydrologic datasets (Nalley et al. 2012) but have not been applied for drought forecasting using the EDI.”