Literature DB >> 30706265

Delimitation of groundwater zones under contamination risk using a bagged ensemble of optimized DRASTIC frameworks.

Rahim Barzegar1,2, Asghar Asghari Moghaddam3, Jan Adamowski4, Amir Hossein Nazemi5.   

Abstract

Developing a reliable groundwater vulnerability and contamination risk map is very important for groundwater management and protection. This study aims to compare various modified DRASTIC vulnerability frameworks based on rate calibration using the Wilcoxon rank-sum test (WRST), frequency ratio (FR) and weight optimization using the correlation coefficient (CC), the analytic hierarchy process (AHP), and genetic algorithms (GA), as well as to introduce, for the first time, an aggregated approach based on a bagging ensemble to develop a combined modified DRASTIC model. This research was conducted in the Khoy plain, NW Iran. To develop a typical DRASTIC map, seven DRASTIC data layers were generated, weighted, and then overlaid in ArcGIS. The nitrate (NO3) concentrations at 54 sites in the study area were used to validate the models by calculating the correlation coefficient (r) between the vulnerability/risk indices and NO3 concentrations. The calculated r value for the typical DRASTIC was 0.12. A sensitivity analysis reveals that the impact of the vadose zone and conductivity parameters with mean variation indices of 22.2 and 7.5%, respectively, have the highest and lowest influence on aquifer vulnerability. The r values increased for all the optimized frameworks. The results show that the WRST and GA methods are the most effective methods for calibration and optimization of DRASTIC rates and weights, with the WRST-GA-DRASTIC model obtaining an r value of 0.64. A bagging ensemble model was employed to combine the advantages of each standalone model. The bagging ensemble model yields an r value of 0.67. The ensemble model has the potential to increase the r value further than both the standalone optimized frameworks and the typical DRASTIC approach. In terms of spatial distribution class area (%), the bagging ensemble-DRASTIC model demonstrates that the moderate and low contamination risk classes with 16.4 and 23.1% of the total area cover the lowest and highest parts of the plain.

Entities:  

Keywords:  Bagging ensemble; DRASTIC method; Groundwater risk map; Iran; Optimization

Mesh:

Substances:

Year:  2019        PMID: 30706265     DOI: 10.1007/s11356-019-04252-9

Source DB:  PubMed          Journal:  Environ Sci Pollut Res Int        ISSN: 0944-1344            Impact factor:   4.223


  10 in total

1.  Decision threshold adjustment in class prediction.

Authors:  J J Chen; C-A Tsai; H Moon; H Ahn; J J Young; C-H Chen
Journal:  SAR QSAR Environ Res       Date:  2006-06       Impact factor: 3.000

2.  Assessment of intrinsic vulnerability to contamination for Gaza coastal aquifer, Palestine.

Authors:  Mohammad N Almasri
Journal:  J Environ Manage       Date:  2007-03-27       Impact factor: 6.789

3.  Factor weighting in DRASTIC modeling.

Authors:  F A L Pacheco; L M G R Pires; R M B Santos; L F Sanches Fernandes
Journal:  Sci Total Environ       Date:  2014-10-23       Impact factor: 7.963

4.  Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models.

Authors:  Rahim Barzegar; Elham Fijani; Asghar Asghari Moghaddam; Evangelos Tziritis
Journal:  Sci Total Environ       Date:  2017-04-29       Impact factor: 7.963

5.  Assessment and validation of groundwater vulnerability to nitrate based on a modified DRASTIC model: a case study in Jilin City of northeast China.

Authors:  Huan Huan; Jinsheng Wang; Yanguo Teng
Journal:  Sci Total Environ       Date:  2012-09-10       Impact factor: 7.963

6.  Groundwater vulnerability assessment in agricultural areas using a modified DRASTIC model.

Authors:  Mahmood Sadat-Noori; Kumars Ebrahimi
Journal:  Environ Monit Assess       Date:  2015-12-09       Impact factor: 2.513

7.  Calibration of groundwater vulnerability mapping using the generalized reduced gradient method.

Authors:  Alper Elçi
Journal:  J Contam Hydrol       Date:  2017-11-06       Impact factor: 3.188

8.  Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms.

Authors:  Rahim Barzegar; Asghar Asghari Moghaddam; Ravinesh Deo; Elham Fijani; Evangelos Tziritis
Journal:  Sci Total Environ       Date:  2017-11-30       Impact factor: 7.963

9.  Hydrogeochemistry and water quality of the Kordkandi-Duzduzan plain, NW Iran: application of multivariate statistical analysis and PoS index.

Authors:  Shahla Soltani; Asghar Asghari Moghaddam; Rahim Barzegar; Naeimeh Kazemian; Evangelos Tziritis
Journal:  Environ Monit Assess       Date:  2017-08-18       Impact factor: 2.513

10.  Groundwater Vulnerability Assessment of the Pingtung Plain in Southern Taiwan.

Authors:  Ching-Ping Liang; Cheng-Shin Jang; Cheng-Wei Liang; Jui-Sheng Chen
Journal:  Int J Environ Res Public Health       Date:  2016-11-23       Impact factor: 3.390

  10 in total

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