Tiyasha Tiyasha

TL;DR: The proposed hybrid AI models provided a reliable and robust computer aid technology for sediment Pb prediction that contribute to the best knowledge of environmental pollution monitoring and assessment.

TL;DR: In this article , the authors provide a clear understanding of the state-of-the-art ML models implemented for GWL modeling and the milestones achieved in this domain, as well as recommendations for possible future research directions to improve the accuracy of GWL prediction models and enhance the related knowledge.

TL;DR: The XGBoost model validated against a diversity of data-driven models such as multilinear regression (MLR), support vector machine (SVM), and random forest (RF) and outperforms D2EHPA, EDTA, H2SO4, and NaCl predictors in order.

TL;DR: In this paper, the reliability of four feature selector algorithms (i.e., Boruta, GA, multivariate adaptive regression splines (MARS), and extreme gradient boosting (XGBoost) to select the best suited predictor of the applied water quality (WQ) parameters was evaluated.

TL;DR: The applied statistical analysis of the results indicates that ANN and Grid-RF models can be employed as a computer-aided model for monitoring and simulating the adsorption from aqueous solutions by Attapulgite clay.