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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

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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.

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Acknowledgements

The authors would like to thank ABFAR (Mazandaran Rural Water and Sewer Company) to provide the groundwater quality secondary data and help us with data preprocessing.

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Authors and Affiliations

  1. Department of Range and Watershed Management and Dept. of Water Eng. and Environment, Faculty of Natural Resources, University of Guilan, 1144, Sowmeh Sara, Guilan, Iran

    V. Gholami

  2. Torbat-E-Jam Branch, Islamic Azad University, Torbat-e-Jam, Iran

    M. R. Khaleghi

  3. Department of Geoinformatics and Surveying, Faculty of Environmental and Engineering, Southwest Jiaotong University, Chengdu, China

    S. Pirasteh

  4. Water Engineering and Management, Faculty of Engineering Technology, University of Twente, Enschede, the Netherlands

    Martijn J. Booij

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  1. V. Gholami
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  2. M. R. Khaleghi
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  4. Martijn J. Booij
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Contributions

All authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed by [VG], [MRK], and [SP]. The first draft of the manuscript was written by [MRK] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to M. R. Khaleghi.

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Gholami, V., Khaleghi, M.R., Pirasteh, S. et al. Comparison of Self-Organizing Map, Artificial Neural Network, and Co-Active Neuro-Fuzzy Inference System Methods in Simulating Groundwater Quality: Geospatial Artificial Intelligence. Water Resour Manage 36, 451–469 (2022). https://doi.org/10.1007/s11269-021-02969-2

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