Suraj Kumar Bhagat

TL;DR: In this review, each element of the predictive models and their corresponding treatment processes, including its pros and cons, are discussed thoroughly and several research directions, which could bridge the gap in the same domain are proposed and recommended on the basis of the identified research limitations.

TL;DR: The extreme gradient boosting (XGBoost) model is explored as a superior SuperLearning (SL) algorithms for Pb prediction using historical data at the Bramble and Deception Bay stations, Australia.

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: 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: The ANFIS-mSG model was demonstrated as an effective and simple hybrid artificial intelligence model for predicting soil temperature based on univariate air temperature scenario.