A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling

By Vahid Nourani, M. Komasi, Akira Mano. 2009

How researchers see this paper

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

Citations in the corpus

A deep learning framework for financial time series using stacked autoencoders and long-short term memory (2017, background): “First, ANNs do not require prior knowledge of the time series structure because of their black-box properties [74].”
Estimation of Mean Annual Flood in Indian Catchments Using Backpropagation Neural Network and M5 Model Tree (2010, background): “Successful applications of machine learning approaches in water resources management by Dawson and Wilby (1998), Govindaraju (2000), Solomatine and Dulal (2003), Bhattacharya and Solomatine (2005), Cigizoglu and Alp (2006), Altunkaynak (2007), Hong and Pai (2007), Pal and Goel (2007), Singh and Kuma”
Wavelet Bootstrap Multiple Linear Regression Based Hybrid Modeling for Daily River Discharge Forecasting (2014, background): “+2=N-1 (Nourani et al. 2009).”
Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model (2017, methodology): “The wavelet function utilised in this study was adopted from the family of Daubechies mother wavelets (Nourani et al. 2009; Wu et al. 2009) where the DWT process operated as two sets of functions with a high-pass and a low-pass filter.”
Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review (2014, methodology): “…minimum level of decomposition, L, to the number of data points within the time series Ns, was introduced in the literature (Aussem et al., 1998; Nourani et al., 2009b; Wang and Ding, 2003): L ¼ int½log Ns ð1Þ Later, Nourani et al. (2011) criticized the outcome of this formula, stating that,…”