Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity

A. A. Masrur Ahmed(NSW Department of Planning and Environment), Linshan Yang(Northwest Institute of Eco-Environment and Resources), 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(Chinese Academy of Sciences)
Journal of Hydrology
May 1, 2021
10.1016/j.jhydrol.2021.126350
Cited by 144

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
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
Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data
|Remote Sensing|2021|95