Artificial intelligence and machine learning in environmental impact prediction for soil pollution management - case for EIA process - Environmental Advances 17

Aug 4, 2026

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Scientific predictions are central to Environmental Impact Assessment (EIA) because they estimate potential direct, indirect, and cumulative changes in environmental receptors such as soil. In data-sparse regions, decision-making for mitigating complex soil pollution remains difficult, particularly when heavy metals, petroleum hydrocarbons, and physicochemical factors interact. The study develops and evaluates machine learning models using new experimental soil data from Nigeria, compares them against multivariate linear regression, and finds multivariate linear regression underperforms due to nonlinearity. Log-normalization improves model accuracy, while random forest delivers the strongest predictive performance.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages19

Reading Time48 min

AccessFree to download

Abstract

Introduction

Environmental Impact Assessment and soil as a receptor

Soil pollutants and commonly studied parameters

AI/ML for soil impact prediction

Artificial intelligence and machine learning in environmental impact prediction for soil pollution management – case for EIA process Anifowose, B & Anifowose, F Published PDF deposited in Coventry University’s Repository Original citation: Anifowose, B & Anifowose, F 2024, 'Artificial intelligence and machine learning in environmental impact prediction for soil pollution management – case for EIA process', Journal of Environmental Advances, vol. 17, 100554. [https://doi.org/10.1016/j.envadv.2024.100554](https://doi.org/10.1016/j.envadv.2024.100554) [DOI 10.1016/j.envadv.2024.100554](DOI 10.1016/j.envadv.2024.100554)[ ](DOI 10.1016/j.envadv.2024.100554)[ISSN 2666-7657](ISSN 2666-7657) [Publisher: Elsevier](Publisher: Elsevier) This is an Open Access article distributed under the terms of the Creative Commons Attribution License ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Environmental Advances 17 (2024) 100554 Contents lists available at ScienceDirect Environmental Advances journal [homepage: www.sciencedirect.com/journal/environmental-advances](homepage: www.sciencedirect.com/journal/environmental-advances) | Artificial intelligence and machine learning in environmental impact prediction for soil pollution management – case for EIA process Babatunde Anifowosea, b, *, Fatai Anifowosec
a College of Engineering, Environment & Science, Coventry University, Coventry CV1 5FB, UK
b Centre for Agroecology, Water and Resilience (CAWR), Coventry University, Wolston Ln, Ryton-on-Dunsmore, Coventry CV8 3LG, UK c Saudi Arabian Oil Company, Dhahran 31311, Saudi Arabia | | | | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | Keywords:
Machine Learning
Artificial Intelligence Multivariate Linear Regression Sustainability
Environmental Impact Assessment Soil Quality
Environmental Data Science | | Scientific predictions are a key component of Environmental Impact Assessments (EIA), which can indicate the level of change within an environmental sphere (e.g., soil). As part of the EIA process, decision-making in mitigating complex environmental problems such as maintaining soil quality can be challenging, especially in data-sparse locations. Artificial Intelligence (AI) can ameliorate but the literature suggests that the deployment of Machine Learning (ML) techniques in soil research is concentrated mostly in developed countries. The potential of ML in managing soil pollution from complex mixture of heavy metals, petroleum hydrocarbons, and physicochemical factors is rarely explored. To address this research gap, we built robust models that increase the accuracy of impact prediction based on new experimental soil data from a data-sparse region of Africa (i.e., Nigeria). The algorithms applied are artificial neural networks (ANN), support vector regression (SVR), regression tree (RT), and random forest (RF). The study also implemented a multivariate linear regression (MLR) model as a baseline. Key findings include (a) the MLR model performed less than the machine learning models largely due to the nonlinearity of data; (b) Log-normalization helped to improve the predictive capability of all models asthe effects of statistical variability were removed; (c) the RF model had the best performance in terms of correlation coefficient, mean absolute error, and root mean square error, and (d) the machine learning models showed improved performance with increased correlation and lower error between the actual and predicted soil electrical conductivity values. Our results imply that data sparsity may no longer be an excuse for the non-use of quantitative impact prediction in Environmental Impact Assessment (EIA) processes. This could change how EIAs are conducted and enhance sustainability in natural resource exploitation

FAQs

Why are scientific predictions important in the EIA process for soil pollution?

EIA relies on predictions to indicate potential direct, indirect, and cumulative impacts on receptors such as soil, supporting mitigation decisions.

What machine learning models were evaluated for predicting impacts on soil pollution?

The study applies artificial neural networks (ANN), support vector regression (SVR), regression tree (RT), and random forest (RF), alongside multivariate linear regression (MLR) as a baseline.

What were the key findings about model performance and data variability?

MLR performed worse than the ML models, log-normalization improved predictive capability by reducing statistical variability effects, and random forest achieved the best error and correlation metrics.

Aug 4, 2026

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