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Determination of artificial recharge location using analytic hierarchy process and Dempster–Shafer theory

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Abstract

Groundwater is an essential and major source of water in semi-arid and arid regions, and thus, determining potential locations for groundwater recharge, via prompt and accurate techniques, is necessary. The aim of the research is finding the best artificial recharge location for injecting the excess surface water into aquifer in Shiraz watershed comparing fuzzy-AHP method and Dempster–Shafer theory (DST). The former employs membership functions to determine the importance of each factor in artificial recharge and generates fuzzy maps through analytic hierarchy process (AHP), whereas the later takes the level of confidence, management strategy and geographic conditions into account. The outcomes of this study demonstrate the applicability of the proposed methods for the artificial recharge location prediction. The DST is among the foremost tools, which produces an artificial recharge map at a given level of confidence. For Shiraz watershed, three levels of confident of 99.5%, 99%, and 95% were applied and the results were compared with fuzzy-AHP method. The fuzzy-AHP method identified ~ 19.76% and 6.02% of the study area as highly suitable and unsuitable classes, respectively. While in the DST, the method identified ~ 32% of the area as moderately suitable. The comparison of the two methods to determine the best location for artificial recharge showed that the fuzzy-AHP method had a lower accuracy than DST in determining suitable locations for artificial recharge. Additionally, the DST method can generate several maps with different confidence levels that suit the economic conditions and importance of the study area.

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Abbreviations

\(\mu_{A}\) :

Trapezoidal membership function (MF)

\(x\) :

Membership in the fuzzy set A

\(\theta\) :

A finite set of elements

\(\Phi\) :

An empty set

\(\theta\) :

The system malfunction in a, b, or c

\(\Omega^{2}\) :

Interval of [0 1]

\(m\) :

A function of the basic probability assignment (BPA)

\(\lambda \left( {T_{{\text{P}}} } \right)E_{ij}\) :

Positive relative probability

\(\lambda \left( {\overline{{T_{{\text{P}}} }} } \right)E_{ij}\) :

Negative relative probability

λ (Tp)Eij :

Mass functions

A :

Fuzzy set

\(A = \left\{ {a,b} \right\}\) :

The subset of \(\theta\) or \(A \subset \theta\).

CI:

The consistency index

CR:

The consistency ratio

\(m\left( A \right)\) :

The ratio of set \(A\)

MF:

Membership functions

m(Tp)Eij :

The belief function

m \(\overline{{T_{{\text{p}}} }}\) E ij :

Disbelief function

N(L):

The total number of pixels

N(Eij):

The number of pixels in class Eij

N(L ∩ Eij):

The number of pixels labeled as suitable in the class Eij

T p :

Suitable pixels or cells

\(\overline{{T_{{\text{p}}} }}\) :

Pixels or cells labeled as unsuitable pixel

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Acknowledgements

The authors would like to thank Shiraz University for providing financial support (238726-1122) for this study. We are grateful to personnel of Fars Regional Water Organization for their kind assistance.

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Authors and Affiliations

  1. Department of Range and Watershed Management, College of Agriculture and Natural Resources of Darab, Shiraz University, 71946-84471, Shiraz, Iran

    Marzieh Mokarram

  2. Department of Civil and Environmental Engineering and Construction, University of Nevada, Las Vegas, Las Vegas, NV, USA

    Ali Saber

  3. Department of GIS and Remote Sensing, Faculty of Geography, Tehran University, Tehran, Iran

    Parisa Mohammadizadeh

  4. National Oceanic and Atmospheric Administration (NOAA), College Park, MD, 20740, USA

    Ali Abdolali

  5. University Corporation for Atmospheric Research (UCAR), Boulder, CO, 80301, USA

    Ali Abdolali

Authors
  1. Marzieh Mokarram
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  2. Ali Saber
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  3. Parisa Mohammadizadeh
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  4. Ali Abdolali
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Correspondence to Marzieh Mokarram.

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Mokarram, M., Saber, A., Mohammadizadeh, P. et al. Determination of artificial recharge location using analytic hierarchy process and Dempster–Shafer theory. Environ Earth Sci 79, 241 (2020). https://doi.org/10.1007/s12665-020-08994-5

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  • DOI: https://doi.org/10.1007/s12665-020-08994-5

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