Machine learning vs. statistical model for prediction modeling and experimental validation: Application in groundwater permeable reactive barrier width design

Fengshi Guo, Yangmin Ren, Yongyue Zhou, Shiyu Sun, Mingcan Cui, Jeehyeong Khim

Research output: Contribution to journalArticlepeer-review

Abstract

Permeable reactive barrier (PRB) is an effective in-situ technology for groundwater remediation. The important factors in PRB design are the width and reactive material. In this study, the beaded coal mine drainage sludge (BCMDS) was employed as the filling material to adsorb arsenic pollutants in groundwater, aiming to design the width of PRB. The design methods involving traditional continue column experiments and empirical formulas, as well as machine learning (ML) predictions and statistical methods, which are compared with each other. Traditional methods are determined based on breakthrough curves under several conditions. ML method has advantages in predicting the width of mass transfer zone (WMTZ), which simultaneously consider the characteristics of material, pollutant, and environmental conditions, with data collected from articles. After data preprocessing and model optimizing, selected the XGBoost algorithm based on the high accuracy, which shows good prediction for WMTZ (R2 = 0.97, RMSE = 0.15). The experimentally derived WMTZ values were also used to validate the predictions, demonstrating the ML low error rate of 7.04 % and the feasibility. Subsequent statistical analysis of multiple linear regression (MLR) showed the error rate of 39.43 %, interpret superiority of ML due to the complexity of influencing factors and the insufficient precision of math regression. Compared to traditional width design methods, ML can improve design efficiency and save experimental time and manpower. Further expansion of the dataset and optimization of algorithms could enhance the accuracy of ML, overcoming existing limitations and gaining broader applications.

Original languageEnglish
Article number133825
JournalJournal of hazardous materials
Volume469
DOIs
Publication statusPublished - 2024 May 5

Bibliographical note

Publisher Copyright:
© 2024 Elsevier B.V.

Keywords

  • Design
  • Ground water
  • Machine learning
  • Permeable reactive barrier
  • Statistical model

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Waste Management and Disposal
  • Pollution
  • Health, Toxicology and Mutagenesis

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