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Comparison of Self-Organizing Map, Artificial Neural Network, and Co-Active Neuro-Fuzzy Inference System Methods in Simulating Groundwater Quality: Geospatial Artificial Intelligence

Author

Listed:
  • V. Gholami

    (University of Guilan)

  • M. R. Khaleghi

    (Islamic Azad University)

  • S. Pirasteh

    (Southwest Jiaotong University)

  • Martijn J. Booij

    (University of Twente)

Abstract

Water quality experiments are difficult, costly, and time-consuming. Therefore, different modeling methods can be used as an alternative for these experiments. To achieve the research objective, geospatial artificial intelligence approaches such as the self-organizing map (SOM), artificial neural network (ANN), and co-active neuro-fuzzy inference system (CANFIS) were used to simulate groundwater quality in the Mazandaran plain in the north of Iran. Geographical information system (GIS) techniques were used as a pre-processer and post-processer. Data from 85 drinking water wells was used as secondary data and were separated into two splits of (a) 70 percent for training (60% for training and 10% for cross-validation), and (b) 30 percent for the test stage. The groundwater quality index (GWQI) and the effective water quality factors (distance from industries, groundwater depth, and transmissivity of aquifer formations) were implemented as output and input variables, respectively. Statistical indices (i.e., R squared (R-sqr) and the mean squared error (MSE)) were utilized to compare the performance of three methods. The results demonstrate the high performance of the three methods in groundwater quality simulation. However, in the test stage, CANFIS (R-sqr = 0.89) had a higher performance than the SOM (R-sqr = 0.8) and ANN (R-sqr = 0.73) methods. The tested CANFIS model was used to estimate GWQI values on the area of the plain. Finally, the groundwater quality was mapped in a GIS environment associated with CANFIS simulation. The results can be used to manage groundwater quality as well as support and contribute to the sustainable development goal (SDG)-6, SDG-11, and SDG-13.

Suggested Citation

  • V. Gholami & M. R. Khaleghi & S. Pirasteh & Martijn J. Booij, 2022. "Comparison of Self-Organizing Map, Artificial Neural Network, and Co-Active Neuro-Fuzzy Inference System Methods in Simulating Groundwater Quality: Geospatial Artificial Intelligence," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 451-469, January.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:2:d:10.1007_s11269-021-02969-2
    DOI: 10.1007/s11269-021-02969-2
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    References listed on IDEAS

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    1. Mohammad Nikoo & Najmeh Mahjouri, 2013. "Water Quality Zoning Using Probabilistic Support Vector Machines and Self-Organizing Maps," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2577-2594, May.
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    2. Abhinav Kumar Singh & Pankaj Kumar & Rawshan Ali & Nadhir Al-Ansari & Dinesh Kumar Vishwakarma & Kuldeep Singh Kushwaha & Kanhu Charan Panda & Atish Sagar & Ehsan Mirzania & Ahmed Elbeltagi & Alban Ku, 2022. "An Integrated Statistical-Machine Learning Approach for Runoff Prediction," Sustainability, MDPI, vol. 14(13), pages 1-30, July.
    3. Thiago Victor Medeiros Nascimento & Celso Augusto Guimarães Santos & Camilo Allyson Simões Farias & Richarde Marques Silva, 2022. "Monthly Streamflow Modeling Based on Self-Organizing Maps and Satellite-Estimated Rainfall Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2359-2377, May.
    4. Ning Sun & Sai Tang & Ju Zhang & Jiaxin Wu & Hongwei Wang, 2022. "Food Security: 3D Dynamic Display and Early Warning Platform Construction and Security Strategy," IJERPH, MDPI, vol. 19(18), pages 1-16, September.

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