Impact of road transport system on groundwater quality inferred from eXplainable Artificial Intelligence (XAI)

Road networks constitute a vital component of modern society, facilitating rapid transportation and driving economic activities by enabling the smooth movement of goods and people. However, the expansion of road systems carries significant environmental considerations, particularly regarding its impact on groundwater quality. Thus, it is crucial to understand the complex relationship between groundwater quality and the road traffic system. This paper aims to identify the impact of road transport systems on groundwater quality using a data-driven approach. Specifically, road network and groundwater chemistry data in Texas were obtained from an open data portal. This study was carried out in two phases: the explainable artificial intelligence (XAI) modeling phase and the multivariate analysis phase. In the XAI modeling phase, a prediction model was developed using eXtreme Gradient Boosting (XGB), with groundwater chemistry parameters as output features and road transport attributes as input features, i.e., elevation, annual average daily traffic, distance, lane-miles, speed limit and well depth. Furthermore, the relationships between groundwater chemistry parameters and road transport attributes were examined using feature importance and accumulated local effect (ALE). In the multivariate phase, Piper diagrams and principal component analysis (PCA) were utilized to identify the source of the selected groundwater chemistry parameters from the XAI models. The results of the prediction model showed that five groundwater chemistry parameters were significantly impacted by road transport systems with below a MAPE of 0.20, including, pH, temperature, aluminum (Al), bicarbonate (HCO₃⁻), and alkalinity. Additionally, XAI models were developed to understand the relationship between the road transport attributes on five selected parameters. The findings collectively indicated that the Texas groundwater qualities are greatly impacted by road transport systems within a distance of 50-meters and a well depth of 100-meters. This study provides a novel contribution to monitoring point sources of groundwater pollution using XAI techniques.

• Record URL:
  • https://doi.org/10.1016/j.scitotenv.2024.170388
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0048969724005230
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00489697
• Supplemental Notes:
  • © 2024 Elsevier B.V. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Kwak, Kyungwon
  • 0009-0007-9252-2816
  • Lee, Eun Hak
  • 0000-0001-5989-4895
• Publication Date: 2024-3-20

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 170388
• Serial:
  • Science of the Total Environment
  • Volume: 917
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0048-9697
  • Serial URL: http://www.sciencedirect.com/science/journal/00489697

Subject/Index Terms

• TRT Terms: Chemical analysis; Environmental monitoring; Groundwater; Highway transportation; Predictive models; Water quality
• Identifier Terms: eXtreme Gradient Boosting (XGB) algorithm
• Geographic Terms: Texas
• Subject Areas: Data and Information Technology; Environment; Highways; Hydraulics and Hydrology;

Filing Info

• Accession Number: 01913918
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 4 2024 4:59PM