An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions

The development of computer aid models for heavy metals (HMs) simulation has been remarkably advanced over the past two decades. Several machine learning (ML) models have been developed for modeling HMs over the past two decades with outstanding progress. Although there have been a noticeable number of diverse ML models investigations, it is essential to have an informative vision on the progression of those computer aid models. In the current short review covering the simulation of heavy metals in contaminated soil, water bodies and removal from aqueous solution, numerous aspects on the methodological and conceptual HMs modeling are reviewed and discussed in detail. For instance, the limitation of the classical analytical methods, types of heavy metal dataset, necessity for new versions of ML models exploration, HM input parameters selection, ML models internal parameters tuning, performance metrics selection and the types of the modelled HM. The current review provides few outlooks in understanding the underlying od the ML models application for HM simulation. Tackling these modeling aspects is significantly essential for ML developers and environmental scientists to obtain creditability and scientific consistency in the domain of environmental science. Based on the discussed modeling aspects, it was concluded several future research directions, which will promote environmental scientists for better understanding of the underlying HMs simulation. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

The development of computer aid models for heavy metals simulation has made remarkable progress over the past two decades. Despite the significant advancement in modeling HMs via machine learning, attention to data and methodological aspects is still needed to enhance accuracy and application prospects of the models.