Prediction of dissolved oxygen in Surma River by biochemical oxygen demand and chemical oxygen demand using the artificial neural networks (ANNs)A. A. Masrur Ahmed|Journal of King Saud University - Engineering Sciences|2014Cited by 163
Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicityA. A. Masrur Ahmed, Linshan Yang, Ravinesh C. Deo et al.|Journal of Hydrology|2021Cited by 144
Application of adaptive neuro-fuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma RiverA. A. Masrur Ahmed, Syed Mustakim Ali Shah|Journal of King Saud University - Engineering Sciences|2015Cited by 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 DataA. A. Masrur Ahmed, Linshan Yang, Ravinesh C. Deo et al.|Remote Sensing|2021Cited by 95
LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenariosA. A. Masrur Ahmed, Linshan Yang, Ravinesh C. Deo et al.|Stochastic Environmental Research and Risk Assessment|2021Cited by 73