Comprehensive Risk Assessment of Sarkhoon Aquifer Salinization Using a Combination of Machine Learning Models

Document Type : Research Article


1 Assistant Professor, Department of Water Sciences & Engineering, Minab Higher Education Center, University of Hormozgan, Bandar Abbas, Iran

2 Assistant Professor, Department of Natural Resources Engineering, Faculty of Agriculture and Natural Resources, University of Hormozgan, Bandar Abbas, Iran

3 Assistant Professor, Hormoz Study and Research Center, University of Hormozgan, Bandar Abbas, Iran


Risk assessment of aquifer salinization is of great importance especially in regions near the coast. In this study, it was attempted to develop a comprehensive framework for salinity risk assessment for Sarkhoon aquifer, Hormozgan province by combining the aquifer vulnerability potential model and machine learning algorithms. In the first step, the input layers required for the generation of the aquifer vulnerability potential map were prepared based on DRASTIC model and combined. Then, the map of salinization hazard occurrence probability was obtained by using three machine learning models of Random Forest, Extreme Gradient Boosting (XGBoost), and Bayesian Additive Regression Trees (BART) by considering 12 factors affecting groundwater including topographic wetness, soil, vegetation and other factors. Prior to modeling, a collinearity test was performed on the data and it was observed that there was no collinearity between the models’ input parameters. Evaluation of the modeling performance with the receiver operating characteristic (ROC) curve indicated that all three algorithms had very good accuracies with area under curve (AUC) values higher than 90%. Thus, all three models were combined based on their AUC values to produce a united map for the probability of salinization hazard occurrence. Finally, the map of salinization risk was generated based on the values for the vulnerability, salinity and hazard occurrence probability. The obtained risk map showed that the eastern part of the aquifer has very high salinization risk which is due to the high concentration of agricultural land in this part of the plain. The results of this study revealed that achieving a reliable map for assessing aquifer salinization risk is possible by combining machine learning models.


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Volume 7, Issue 1
April 2020
Pages 147-163
  • Receive Date: 06 August 2019
  • Revise Date: 11 February 2020
  • Accept Date: 11 February 2020
  • First Publish Date: 20 March 2020