Development of DRASTIC model using artificial intelligence on the potential of aquifer contamination in semi-arid regions

Document Type : Research Article

Authors

1 Master of Science (MSc), Civil Engineering, Water and Hydraulic Structures, Young Researchers and Elite Club, Mashhad Branch, Islamic Azad University, Mashhad, Iran

2 MSc.Faculty of Surveying and Geomatics Engineering, University of Tehran, Tehran, Iran

3 Department of Civil Engineering, K. N. Toosi University of Technology, Tehran,Iran

4 Assist. Prof. at Dept. of Civil Engineering, University of Birjand, Birjand, Iran

Abstract

Due to rapid economic growth and over-exploitation of groundwater, nitrate contamination in groundwater has become very serious. The main purpose of this study is to develop a DRASTIC model to identify the vulnerability of groundwater to nitrate contamination. Therefore, the standard DRASTIC model was presented considering the land use factor (DRASTIC-LU model) to demonstrate the vulnerability of groundwater. The novelty of the present study is the development of DRASTIC and DRASTIC-LU models by support vector machine (SVM) to prevent the error of overlap and index methods. For implementation and validation of the models, 21 samples of observation wells were collected in Birjand plain aquifer. RMSE values for DRASTIC, DRASTIC-LU, DRASTIC+SVM, and DRASTIC-LU+SVM models were calculated to be 0.821, 0.743, 0.612, and 0.490, respectively, which was found that the hybrid models using SVM shows a better correlation between the amount of vulnerability and nitrate contamination. It was also found that the DRASTIC-LU+SVM model has a higher accuracy for assessing the vulnerability of groundwater to nitrate.

Keywords

Main Subjects

References

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Iranian journal of Ecohydrology
Volume 8, Issue 3
October 2021
Pages 651-665

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History

  • Receive Date: 17 February 2021
  • Revise Date: 16 June 2021
  • Accept Date: 18 June 2021
  • First Publish Date: 22 June 2021
  • Publish Date: 23 September 2021

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