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K-Fold and State-of-the-Art Metaheuristic Machine Learning Approaches for Groundwater Potential Modelling

Author

Listed:
  • Alireza Arabameri

    (Tarbiat Modares University)

  • Aman Arora

    (Chandigarh University
    Jamia Millia Islamia)

  • Subodh Chandra Pal

    (The University of Burdwan)

  • Satarupa Mitra

    (Chandigarh University)

  • Asish Saha

    (The University of Burdwan)

  • Omid Asadi Nalivan

    (Gorgan University of Agricultural Sciences and Natural Resources (GUASNR))

  • Somayeh Panahi

    (Technical and Vocational University (TVU))

  • Hossein Moayedi

    (Ton Duc Thang University
    Ton Duc Thang University)

Abstract

Groundwater being an essential resource is not easily available in some parts of the world. The present study, aimed at procuring better prediction maps for groundwater potential zones, is based on a novel approach combining the use of k-fold cross-validation method and the implementation of four scenarios, each comprising of six machine learning models, ANFIS (Adaptive Neuro Fuzzy Inference System) and five other ensembles of it, ANFIS-Firefly, ANFIS-Bees, ANFIS-GA, ANFIS-DE and ANFIS-ACO. Ada Boost Model has played a vital role in determining the collinearity among the fourteen conditioning factors, which are, Lithology, Slope, TST, TRI, LULC, HAND, Curvature, Distance to Stream, Distance to Fault, Rainfall, Fault Density, Drainage Density, Elevation and Aspect. The AUCROC (Area Under Curve – Receiver Operating Characteristics) approach was employed as a model evaluation metric along with Accuracy, Sensitivity and Specificity. Among the models, ANFIS-DE showed the most promising results, acquiring the highest average values among the four scenarios for AUC (0.934), Accuracy (0.987), Sensitivity (0.985) and Specificity (0.985). Promising results of this study gives the necessary incentive for further applying this approach for groundwater zonation of other areas of the world as well as other areas of hydrogeological studies.

Suggested Citation

  • Alireza Arabameri & Aman Arora & Subodh Chandra Pal & Satarupa Mitra & Asish Saha & Omid Asadi Nalivan & Somayeh Panahi & Hossein Moayedi, 2021. "K-Fold and State-of-the-Art Metaheuristic Machine Learning Approaches for Groundwater Potential Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1837-1869, April.
  • Handle: RePEc:spr:waterr:v:35:y:2021:i:6:d:10.1007_s11269-021-02815-5
    DOI: 10.1007/s11269-021-02815-5
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    References listed on IDEAS

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    1. Han, Congying & Zhang, Baozhong & Chen, He & Wei, Zheng & Liu, Yu, 2019. "Spatially distributed crop model based on remote sensing," Agricultural Water Management, Elsevier, vol. 218(C), pages 165-173.
    2. Tao Wu & Jinde Cao & Lianglin Xiong & Haiyang Zhang, 2019. "New Stabilization Results for Semi-Markov Chaotic Systems with Fuzzy Sampled-Data Control," Complexity, Hindawi, vol. 2019, pages 1-15, November.
    3. Fu, Xiuwen & Yang, Yongsheng, 2020. "Modeling and analysis of cascading node-link failures in multi-sink wireless sensor networks," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
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    Cited by:

    1. Xin Hao & Heng Lyu & Ze Wang & Shengnan Fu & Chi Zhang, 2022. "Estimating the spatial-temporal distribution of urban street ponding levels from surveillance videos based on computer vision," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 1799-1812, April.
    2. Zahra Dashti & Mohammad Nakhaei & Meysam Vadiati & Gholam Hossein Karami & Ozgur Kisi, 2023. "Estimation of Unconfined Aquifer Transmissivity Using a Comparative Study of Machine Learning Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(12), pages 4909-4931, September.
    3. Duong Hai Ha & Phong Tung Nguyen & Romulus Costache & Nadhir Al-Ansari & Tran Phong & Huu Duy Nguyen & Mahdis Amiri & Rohit Sharma & Indra Prakash & Hiep Le & Hanh Bich Thi Nguyen & Binh Thai Pham, 2021. "Quadratic Discriminant Analysis Based Ensemble Machine Learning Models for Groundwater Potential Modeling and Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(13), pages 4415-4433, October.

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