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Assessment of groundwater vulnerability using genetic algorithm and random forest methods (case study: Miandoab plain, NW of Iran)

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Abstract

One of the appropriate ways to prevent groundwater contamination is identifying the vulnerable areas of the aquifers. The DRASTIC framework, for assessing the intrinsic vulnerability of the aquifer, is a common method which uses a specific parameter’s weight and a uniform distributed contaminant in overall the aquifer. Therefore, it should be calibrated for specific aquifer and contaminant distribution conditions. In this research, random forest (RF) and genetic algorithm (GA) methods were used for DRASTIC framework optimization in Miandoab plain (NW of Iran). In optimizing the basic DRASTIC framework (BDF) using GA, decision variables are the weight of DRASTIC parameters and weight values for each data layer are the outputs of the optimization. The final optimized map (BDF-GA map) was obtained using overlaying the layers with optimized weights based on the GA method. In optimization of BDF using RF, the model is made up of from 1 to 100 trees and the m parameter or split variables was optimized by changing the number of variables between one and the maximum variables of each subset. Also, the feature selection method is used to reduce the dimensions and increase the accuracy of the model. To induct the nitrate contaminant model, raster layer data of 7 BDF parameters, together with the target variable (VI of BDF map), were used. In the final step, variables’ importance was identified by the RF method and then, the vulnerability map was obtained based on variable importance. In validation and comparison of methods with CI and ROC methods, the BDF-RF method with the higher CI and ROC values was ranked as the most accurate approach in groundwater vulnerability evaluation. The optimized map using the RF method (BDF-RF map) showed that 14.5, 13, 18, 26.5, and 28% of the plain are located in areas with very low, low, moderate, high, and very high vulnerability categories, respectively.

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Abbreviations

AUC:

Area under curve

BDF:

Basic DRASTIC framework

GA:

Genetic algorithm

BDF-GA:

Basic DRASTIC framework-genetic algorithm

BDF-RF:

Basic DRASTIC framework-random forest

CI:

Correlation index

FS:

Feature selection

FPR:

False positive rate

RF:

Random forest

OOB:

Out of bag

PCSM:

Point count system models

ROC:

Receiver operating characteristic

MSE:

Mean square error

TPR:

True positive rate

VI:

Vulnerability index

WARWA:

West Azerbaijan Regional Water Authority

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Authors and Affiliations

  1. Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran

    Hossein Norouzi & Asghar Asghari Moghaddam

  2. Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parma, Italy

    Fulvio Celico

  3. Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, East Azerbaijan, Iran

    Jalal Shiri

  4. Center for Excellence of Hydroinformatics, Faculty of Civil Eng., University of Tabriz, Tabriz, Iran

    Jalal Shiri

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  1. Hossein Norouzi
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  2. Asghar Asghari Moghaddam
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  3. Fulvio Celico
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  4. Jalal Shiri
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Contributions

Hossein norouzi: writing—original draft, writing—review and editing, methodology, software, doing field works and collecting water samples, analyzing water samples in laboratory; Asghar Asghari Moghaddam: supervision, writing—original draft, writing—review and editing, investigation, methodology, doing field works and collecting water samples, analyzing water samples in laboratory; Fulvio Celico: writing—review and editing, methodology; Jalal Shiri: writing—review and editing.

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Correspondence to Asghar Asghari Moghaddam.

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Norouzi, H., Moghaddam, A.A., Celico, F. et al. Assessment of groundwater vulnerability using genetic algorithm and random forest methods (case study: Miandoab plain, NW of Iran). Environ Sci Pollut Res 28, 39598–39613 (2021). https://doi.org/10.1007/s11356-021-12714-2

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