Groundwater risk assessment based on optimization framework using DRASTIC method

Seyedeh Mahboobeh Jafari, Mohammad Reza Nikoo*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

46 Citations (Scopus)


Due to anthropogenic influences and large amounts of pollutant released into the groundwater, it is vital to investigate groundwater quality and to characterize susceptible areas to contamination. In this paper, a new optimization-based methodology is proposed for determining groundwater risk using DRASTIC model based on genetic algorithm optimization model and Wilcoxon test. The correlation coefficient between DRASTIC/modified DRASTIC indices and nitrate concentrations in monitoring wells is used as a criteria for evaluating the efficiency of the proposed models. In this regard, because of the unsatisfactory original DRASTIC’s result, sensitivity analysis, genetic algorithm (GA), and Wilcoxon test (1945) are carried out to tackle the subjectivity associated with the original DRASTIC model and obtain better and reliable results. The results indicate that application of Wilcoxon test and GA optimization outperforms the others. Consequently, the correlation coefficient increased remarkably as compared to the original DRASTIC model (from 0.57 to 0.82). The proposed optimization process is adaptable to be applied in different case studies; mainly since it has the ability to optimize the weights of the model based on hydrogeological characteristics of the aquifer. Finally, the risk maps of the models are prepared using ArcGIS® to determine the most vulnerable areas.

Original languageEnglish
Article number742
JournalArabian Journal of Geosciences
Issue number20
Publication statusPublished - Dec 1 2016


  • Genetic algorithm
  • Groundwater risk
  • Modified DRASTIC
  • Shiraz plain
  • Spearman’s correlation
  • Wilcoxon test

ASJC Scopus subject areas

  • Environmental Science(all)
  • Earth and Planetary Sciences(all)


Dive into the research topics of 'Groundwater risk assessment based on optimization framework using DRASTIC method'. Together they form a unique fingerprint.

Cite this