Ahmed El-Shafie

TL;DR: This paper explores the state-of-the-art application of AI in stream-flow forecasting, focusing on defining the data-driven of AI, the advantages of complementary models, as well as the literature and their possible future application in modeling and forecasting stream- flow.

TL;DR: A two-tier approach is proposed for improving the stochastic modeling of MEMS-based inertial sensor errors using autoregressive processes at the raw measurement level and enhancing the positioning accuracy during GPS outages by nonlinear modeling of INS position errors at the information fusion level using neuro-fuzzy modules, which are augmented in the Kalman filtering INS/GPS integration.

TL;DR: In this article, the authors highlight up-to-date methods for the removal of heavy metals from water using the technique of adsorption, focusing on one particular technique, involving carbon nanotubes (CNTs).

TL;DR: In this article, the potential of a relatively new data-driven method, namely the extreme learning machine (ELM) method, was explored for forecasting monthly stream-flow discharge rates in the Tigris River, Iraq.

TL;DR: A Neuro-Fuzzy Inference System (WDT-ANFIS) based augmented wavelet de-noising technique has been recommended that depends on historical data of the water quality parameter and exhibited a significant improvement in predicting accuracy for all theWater quality parameters and outperformed all the recommended models.