New double decomposition deep learning methods for river water level forecasting
A. A. Masrur Ahmed(The University of Melbourne), Linshan Yang(Tianjin Medical University Eye Hospital), Ravinesh C. Deo(University of Southern Queensland), Nawin Raj(University of Southern Queensland), Qi Feng(Northwest Institute of Eco-Environment and Resources), Afshin Ghahramani(University of Southern Queensland), Zhenliang Yin(Northwest Institute of Eco-Environment and Resources)
Cited by 70
Related Papers
Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq
|Journal of Hydrology|2016|324
Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model
|Atmospheric Research|2016|299
Prediction of dissolved oxygen in Surma River by biochemical oxygen demand and chemical oxygen demand using the artificial neural networks (ANNs)
|Journal of King Saud University - Engineering Sciences|2014|163
Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity
|Journal of Hydrology|2021|144
Application of adaptive neuro-fuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma River
|Journal of King Saud University - Engineering Sciences|2015|134