Advanced Machine Learning Models for Predicting Diffusion of Pollution in Soils

Shaymaa Alsamia; Edina KOCH; Katalin Bene

doi:10.30572/2018/KJE/170201

Authors

Shaymaa Alsamia Faculty of Engineering, University of Kufa, Iraq
Edina KOCH Department of Structural and Geotechnical Engineering, Széchenyi István University, Hungary
Katalin Bene Department of Transport Infrastructure and Water Resources Engineering, Faculty of Architecture, Civil and Transportation Engineering, Széchenyi István University, Hungary

DOI:

https://doi.org/10.30572/2018/KJE/170201

Keywords:

Pollution Diffusion, Soil Contamination, Machine Learning Models, Environmental Modeling, Predictive Analytics

Abstract

the infiltration of hazardous chemicals into the soil causes soil pollution which poses significant risks to ecosystems and human health. For this reason, accurate predicting the diffusion of pollution in soils is important and critical for monitoring and protection the environmental state. In this study we have compared advanced machine learning ML models to predict vertical and horizontal pollution diffusion using complex and multimodal soil experimental datasets. Support vector regression, linear regression, gradient boosting regression, xgboost regression, k-nearest neighbours, and artificial neural networks were employed to build predicted models and compared with each other. The comparison criteria are measuring mean squared error, root mean squared error, mean absolute error, and R-squared as the metrics used to evaluate the predictive models performance. The observed results demonstrate that ensemble methods XGBoost and random forest, outperform other models in predicting pollution diffusion while XGBoost achieving the highest accuracy. On the other hand, linear regression was the least effective while k-nearest neighbours and artificial neural networks showed moderate performance

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