Predicting Groundwater Capture Zone Characteristics Using Fuzzy Inference System(FIS), Case study: Abarkooh Aquife

Document Type : Research Paper

Authors

1 Faculty of earth Sciences, Shiraz university, Iran

2 Faculty of Earth Sciences, Shahrood University of Technology, Shahrood, Iran

Abstract

Groundwater is a vital resource for human water supply, making the study of well capture zones critical, particularly for anthropogenic water sources and water quality management. Capture zones, also known as wellhead protection areas, are influenced by numerous factors, including pumping rate, hydraulic conductivity, groundwater gradient, and other hydrogeological parameters. Various methods exist for calculating capture zones, ranging from analytical approaches to advanced numerical models, and these methods continue to evolve. This research introduces, for the first time, the application of a Fuzzy Inference System (FIS) to predict both the size and elongation of capture zones. Key input parameters include annual well discharge (measured in million cubic meters, MCM), hydraulic conductivity, groundwater gradient, and aquifer thickness. Results from the WhAEM software were used as target values to validate the FIS predictions. The findings reveal strong correlations between the FIS predictions and the WhAEM results, with correlation
coefficients (R) of 0.92 for capture zone size and 0.73 for elongation coefficient. These results underscore the effectiveness of fuzzy logic in accurately predicting critical hydrogeological parameters, offering a robust alternative method for capture zone analysis. These results underscore the effectiveness of fuzzy logic in accurately predicting critical hydrogeological parameters, offering a robust alternative method for capture zone analysis.

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Article Title [Persian]

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Altunkaynak, A., 2010. A predictive model for well loss using fuzzy logic approach. Hydrological processes, 24 (17): 2400-2404.
Chen, W., Panahi, M., Pourghasemi, H. R., 2017. Performance evaluation of gis-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (anfis) with genetic algorithm (ga),differential evolution (de), and particle swarm optimization (pso) for landslide spatial modelling.Catena, 157: 310-324.
Esbati, M., Ahmadieh Khanesar, M., Shahzadi, A., 2018. Modeling level change in lake urmia using hybrid artificial intelligence approaches. Theoretical and Applied Climatology, 133: 447-458.
Firat, M. and Gu¨ngo¨r, M., 2008. Hydrological time-series modelling using an adaptive neuro-fuzzy inference system. Hydrological Processes: An International Journal, 22(13): 2122-2132.
Fukuda, T., Shimojima, K., Arai, F., Matsuura, H., 1993. Multisensor integration system based on fuzzy inference and neural network. Information Sciences, 71(1-2): 27-41.
Goodarzi, M. and Eslamian, S. S., 2019. Evaluation of whaem and modflow models to determine the protection zone of drinking wells. Environmental Earth Sciences, 78(6):195.
Gupta, A. K., 2021. Fuzzy logic and their application in different areas of engineering science and research: A survey. International Journal of Scientific Research in Science and Technology, 8(2): 71-75.
Jafari, H., Moradi Nazarpoor, S., Niknam, M. S., Bagheri, R., Zarei Doudeji, S., 2023. Delineating capture zone of the production wells in abarkooh aquifer (central iran) using whaem model and statistical method of multivariate regression. Geopersia, 13(2): 357-363.
Keskin, M. E., Taylan, D., Terzi, O., 2006. Adaptive neural-based fuzzy inference system (anfis) approach for modelling hydrological time series. Hydrological sciences journal, 51 (4): 588-598.
Moradi Nazarpoor, S., Rezaei, M., and Mali, F., 2024. A new fuzzy method for investigating the effects of dam on aquifer: case study of rudbal dam, south of iran. Scientific Reports, 14(1):14503.
Nourani, V., Maleki, S., Najafi, H., Baghanam, A. H., 2024. A fuzzy logic-based approach for groundwater vulnerability assessment. Environmental Science and Pollution Research, 31(12):18010-18029.
Pham, Q. B., Mohammadi, B., Moazenzadeh, R., Heddam, S., Zola´, R. P., Sankaran, A., Gupta, V., Elkhrachy, I., Khedher, K. M., Anh, D. T., 2023. Prediction of lake water-level fluctuations using adaptive neuro-fuzzy inference system hybridized with metaheuristic optimization algorithms. Applied water science, 13(1):13.
Siarkos, I. and Latinopoulos, P., 2012. Delineation of wellhead protection zones for the control of point pollution sources in the aquifer of n. moudania, greece. Eur. Water, 40: 3-17.
Sugeno, M., 1985. An introductory survey of fuzzy control. Information sciences, 36 (1-2): 59-83.