Salah L. Zubaidi

TL;DR: In this paper, the authors applied a novel methodology that includes data pre-processing and an Artificial Neural Network (ANN) optimized with the Backtracking Search Algorithm (BSA-ANN) to estimate monthly water demand in relation to previous water consumption.

TL;DR: In this article, a new electrocoagulation (EC) was applied to remove Escherichia coli (E. coli) from wastewater, considering the effects of different parameters such as treatment time, inter-electrode distance, and current density.

TL;DR: A novel combined methodology including, firstly, data pre-processing techniques were employed to decompose the time series of water and climatic factors by using empirical mode decomposition and identifying the best model input via tolerance to avoid multi-collinearity, and the performance of the hybrid model SMA-ANN is better than ANN based on the range of statistical criteria.

TL;DR: In this article, the reliability and capability of a combination of techniques, including Singular Spectrum Analysis (SSA) and Artificial Neural Networks (ANNs), to accurately predict long-term, monthly water demands was investigated.

TL;DR: This study employs industrial by-products (bottom ash (BA), as a cost-effective and eco-friendly alternative, to remediate water from phosphate in the presence of competitor ions (humic acid).