Modeling groundwater quality parameters using adaptive neural fuzzy inference system (ANFIS) (Case study of Markazi Province)

Minaei, Mojde; Aghamohammadi, Hossein; Vahidnia, Mohammad Hasan; Neshat, Amin Reza; Behzadi, Saeed

doi:10.22061/jrsgr.2026.113229.1132

Articles in Press

Document Type : Original Research Paper

Authors

¹ Department of Remote Sensing and GIS, Faculty of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

² Center for Remote Sensing and GIS Research, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran

³ Department of Surveying Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran

https://doi.org/10.22061/jrsgr.2026.113229.1132

Abstract

Background and Objectives: Rapid population growth, industrial and agricultural expansion, and the limited availability of surface water resources have significantly increased dependence on groundwater in arid and semi-arid regions of Iran. Groundwater serves as a vital source of water for domestic consumption, agricultural irrigation, and industrial development, particularly in regions where alternative water resources are scarce. However, groundwater quality has been increasingly threatened by various pollutants, particularly dissolved ions such as sodium, sulfate, and total dissolved solids (TDS), originating from both natural hydrogeochemical processes and anthropogenic activities. Elevated concentrations of these parameters can reduce water suitability for drinking and irrigation purposes and may have adverse environmental and socioeconomic impacts. Understanding the complex relationships among groundwater quality parameters is therefore essential for effective water resource management and long-term sustainability. In recent years, artificial intelligence-based techniques have emerged as powerful tools for modeling environmental systems characterized by nonlinear and uncertain relationships. The primary objective of this study is to model and analyze the interactions among major groundwater quality pollutants in Markazi Province using the Adaptive Neuro-Fuzzy Inference System (ANFIS), with the aim of identifying temporal variations in groundwater quality and providing a scientific basis for sustainable groundwater management.
Methods: In this study, groundwater quality data including sodium, sulfate, and TDS were collected from monitoring wells distributed across Markazi Province for three time periods corresponding to the years 2017, 2020, and 2023. Prior to modeling, several preprocessing procedures, including data screening, normalization, and outlier removal, were applied to improve data quality and ensure reliable model development. The ANFIS modeling framework was implemented in the MATLAB environment to capture the nonlinear relationships among the selected groundwater quality parameters. Different fuzzy inference structures, including Grid Partitioning, Fuzzy C-Means (FCM), and Subtractive Clustering, were evaluated in combination with hybrid and backpropagation optimization algorithms. The dataset was divided into training (20%) and testing (80%) subsets to assess model generalization capability. Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²).
Findings: The modeling results demonstrated that ANFIS is capable of effectively identifying and reproducing complex nonlinear relationships among groundwater quality parameters. The evaluation metrics indicated satisfactory predictive performance for most of the investigated models. In general, models based on Grid Partitioning combined with hybrid optimization produced more reliable results in the earlier study periods, exhibiting lower prediction errors and higher coefficients of determination. The results also revealed that model performance varied among the investigated years, reflecting temporal changes in groundwater quality conditions and data characteristics. While positive R² values and relatively low RMSE and MAE values confirmed the effectiveness of ANFIS in most cases, some models developed for the 2023 dataset showed weaker performance, indicating increased complexity and variability in groundwater quality patterns. Nevertheless, the overall findings confirmed the capability of ANFIS to model groundwater quality parameters with acceptable accuracy.
Conclusion: Overall, the findings of this study confirm that ANFIS is a reliable and efficient tool for groundwater quality modeling and can be applied as an intelligent decision-support system for regional groundwater monitoring and management. The results highlight the importance of selecting appropriate fuzzy inference structures and optimization algorithms to achieve accurate predictions. Considering the critical role of groundwater resources in supplying drinking water and supporting agricultural activities in Markazi Province, the application of ANFIS-based models can enhance management strategies, assist in identifying vulnerable and critical areas, and contribute to the prevention of further groundwater quality degradation. Furthermore, the use of similar intelligent modeling approaches in other regions with comparable hydrogeological conditions can improve groundwater resource management, support sustainable development goals, and contribute to long-term environmental sustainability.

Keywords

Main Subjects

Geo-spatial Information System

COPYRIGHTS

(https://creativecommons.org/licenses/by-nc/4.0/)

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Journal of Remote Sensing and Geoinformation Research

Modeling groundwater quality parameters using adaptive neural fuzzy inference system (ANFIS) (Case study of Markazi Province)

References

References

Articles in Press, Accepted Manuscript
Available Online from 13 July 2026

Modeling groundwater quality parameters using adaptive neural fuzzy inference system (ANFIS) (Case study of Markazi Province)

References

References

Articles in Press, Accepted Manuscript Available Online from 13 July 2026

Articles in Press, Accepted Manuscript
Available Online from 13 July 2026