Groundwater vulnerability assessment in central Iran - Integration of GIS-based DRASTIC model and a machine learning approach

Aug 5, 2026

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The study evaluates groundwater susceptibility in central Iran by integrating a GIS-based DRASTIC vulnerability model with machine learning. DRASTIC inputs include water table depth, net recharge, aquifer and soil media, topography, vadose zone impact, and hydraulic conductivity to produce vulnerability maps. Machine-learning algorithms (SVM, RF, and GLM) optimized the DRASTIC approach using the SDM package in R, with model performance judged via ROC curves. Results indicate ~40% high vulnerability and ~30% moderate pollution risk; RF achieved the best prediction with AUC 0.98.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

Groundwater importance and contamination concerns

Need for vulnerability and pollution risk assessment

Vulnerability assessment and mapping

Role of vulnerability maps

Data quality and interpretation

Groundwater for Sustainable Development 23 (2023) 101037 Contents lists available at ScienceDirect Groundwater for Sustainable Development journal [homepage:](homepage: www.elsevier.com/locate/gsd)[ www.elsevier.com/locate/gsd](homepage: www.elsevier.com/locate/gsd) | Research paper
Groundwater vulnerability assessment in central Iran: Integration of GIS-based DRASTIC model and a machine learning approach
Zeynab Karimzadeh Motlagha, Reza Derakhshani b, c, *, Mohammad Hossein Sayadid
a Department of Natural Resources, Isfahan University of Technology, Isfahan, Iran b Department of Geology, Shahid Bahonar University of Kerman, Kerman, Iran c Department of Earth Sciences, Utrecht University, Netherlands
d Faculty of Natural Resources and Environment, Shahid Bahonar University of Kerman, Iran | | | --- | --- | | H I G H L I G H T S
• Novel risk assessment framework for groundwater contamination in Iran using chemical and statistical analysis.
• New hybrid groundwater vulnerability model combining DRASTIC and machine learning for management strategies.
• Innovative methodology using ML models (SVM, GLM, RF) for groundwater pollution risk assessment.
• Vulnerability mapping key for sustainable groundwater development. A R T I C L E I N F O
Keywords:
Groundwater vulnerability assessment DRASTIC model
GIS
Machine learning | G R A P H I C A L A B S T R A C T
A B S T R A C T
The study try to evaluate the susceptibility of groundwater. The DRASTIC model was implemented through GIS. Various input variables, such as water table depth, net recharge, aquifer and soil media, topography, vadose zone impact, and hydraulic conductivity, were evaluated within the model to generate a groundwater vulnerability map. Subsequently, machine-learning algorithms (SVM, RF, and GLM) employed using the SDM package in R software to optimize the DRASTIC method. To assess the performance of groundwater pollution risk models, training and validation datasets were evaluated using the ROC curve. The results revealed that approximately 40% of the study area fell within the high vulnerability range, while around 30% exhibited moderate pollution risk. Evaluation of the machine learning models indicated their effectiveness in model development. The RF model demonstrated the highest predictive power, achieving an AUC of 0.98. Additionally, the GLM and SVM algorithms achieved AUC values of approximately 76%. These algorithms can serve as efficient techniques for evaluating and managing groundwater resources. The findings underscored relatively poor groundwater quality in the study area, with excessive aquifer exploitation by the agricultural sector and infiltration of urban sewage and industrial waste identified as the primary causes of groundwater pollution. The implications of these findings are crucial for devising strategies and implementing preventive measures to mitigate water resource vulnerability and associated health risks in central Iran. | * Corresponding author. Department of Geology, Shahid Bahonar University of Kerman, Kerman, Iran. E-mail address: [r.derakhshani@uu.nl](r.derakhshani@uu.nl) (R. Derakhshani). [https://doi.org/10.1016/j.gsd.2023.101037](https://doi.org/10.1016/j.gsd.2023.101037) Received 15 September 2023; Received in revised form 4 November 2023; Accepted 5 November 2023 Available online 10 November 2023 2352-801X/© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)). 1. Introduction Groundwater plays a critical role as a valuable source of fresh water for human survival and is particularly important for drinking purposes due to its lower risk of surface contamination (Fayaji et al., 2019). The global challenge of ensuring access to safe and high-quality drinking water is well recognized (Iqbal et al., 2023). In arid and semi-arid regi

FAQs

What is the main goal of the study?

To assess groundwater vulnerability and pollution risk in central Iran by integrating a GIS-based DRASTIC model with machine learning methods.

Which variables are used in the GIS-based DRASTIC model?

Water table depth, net recharge, aquifer and soil media, topography, vadose zone impact, and hydraulic conductivity are evaluated to generate the vulnerability map.

How do the machine-learning models perform and which is best?

Performance is assessed using ROC curves on training and validation datasets; the Random Forest model performs best with AUC 0.98, while GLM and SVM achieve AUC values around 76%.

Aug 5, 2026

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