Declining groundwater levels in semi-arid regions threaten long-term water resource security and agricultural sustainability. In this study, two hybrid machine learning–Analytical Hierarchy Process (ML–AHP) models, including the Random Forest–Analytical Hierarchy Process (RF–AHP) and Gene Expression Programming–Analytical Hierarchy Process (GEP–AHP) models, were developed to map groundwater recharge potential in the Neyshabur aquifer in northeastern Iran. Groundwater level data from 38 piezometers over the period 2000–2018 were used to train both the GEP and RF models, and the importance values of independent variables were extracted and incorporated into the AHP framework to reduce reliance on expert judgment in weight assignment. The results showed that the GEP model performed better in temporal groundwater level prediction, achieving an NSE of 0.407 in the testing phase, while the RF model obtained an NSE of 0.193. In contrast, the RF–AHP model showed superior performance in spatial groundwater recharge zoning, with an AUC of 0.797 compared to 0.629 for the GEP–AHP model. The Topographic Wetness Index (TWI) and wet-season NDVI were identified as the most influential controlling factors of groundwater recharge in both models. The estimated annual recharge was 88.2 × 10⁶ m³ (15.6% of annual precipitation) for the RF–AHP model and 74.0 × 10⁶ m³ (13.1% of annual precipitation) for the GEP–AHP model.
Al-Ruzouq, R., Shanableh, A., Yilmaz, A. G., Idris, A., & Mukherjee, S. (2023). Comparative analysis of machine learning and analytical hierarchy analysis for artificial groundwater recharge map development. Environmental Earth Sciences, 82(11), Article 378. https://doi.org/10.1007/s12665-023-11237-y
Eftekhari, M., Haji Elyasi, A., & Eslaminezhad, S. A. (2024). Assessment of machine learning models in GIS for predicting groundwater in semi-arid regions of eastern Iran. Journal of Aquifer and Qanat, 4(2), 49–66. https://doi.org/10.22077/jaaq.2024.7282.1062.[In Persian].
Freeze, R. A., & Cherry, J. A. (1979). Groundwater Prentice-Hall Inc. Eaglewood Cliffs, NJ, 604.
Hussainzadeh, J., Mohammadzadeh, H., & Mohammaddost, A. (2024). Inverse geochemical modeling and hydrogeochemical evolution mechanism of groundwater in Neyshabur Plain, NE Iran. Water Resources, 51(Suppl 2), S306-S319. https://doi.org/10.1134/S0097807824603376
Izady, A., Davary, K., Alizadeh, A., Ziaei, A. N., Akhavan, S., Alipoor, A., Joodavi, A., & Brusseau, M. L. (2015). Groundwater conceptualization and modeling using distributed SWAT-based recharge for the semi-arid agricultural Neishaboor plain, Iran. Hydrogeology Journal, 23(1), 47–68. https://doi.org/10.1007/s10040-014-1219-9
Kaewdum, N., & Chotpantarat, S. (2021). Mapping potential zones for groundwater recharge using a GIS technique in the Lower Khwae Hanuman Sub-Basin Area, Prachin Buri Province, Thailand. Frontiers in Earth Science, 9, Article 717313. https://doi.org/10.3389/feart.2021.717313
Kalali Moghadam, Z. (2024). Evaluation of the effects of water crisis and land subsidence in the rural areas of Neishabur plain. Journal of Geography and Regional Development, 22(2), 169–201. https://doi.org/10.22067/jgrd.2024.86096.1379. [In Persian].
Khoshnevisan, S., Emamgholizadeh, S., Asli Charandabi, M., & Khakzad, S. F. (2025). Comparative evaluation of machine learning algorithms for groundwater level modeling and prediction: A case study of the Shahrood Plain. Journal of Aquifer and Qanat, 6(2), 77–96. https://doi.org/10.22077/jaaq.2025.10441.1132. [In Persian].
Malekinia, M., Javid, A. H., Rahmati, S. H., & Jahangir, A. (2026). Comparison of numerical models and machine learning algorithms with groundwater level prediction approach: A case study in Birjand plain. Journal of Aquifer and Qanat, 6(2), 257–281. https://doi.org/10.22077/jaaq.2026.10568.1133.[In Persian].
Naghibi, S. A., Ahmadi, K., & Daneshi, A. (2017). Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping. Water Resources Management, 31(9), 2761–2775. https://doi.org/10.1007/s11269-017-1660-3
Noori, R., Maghrebi, M., Jessen, S., Bateni, S. M., Heggy, E., Javadi, S., ... & AghaKouchak, A. (2023). Decline in Iran’s groundwater recharge. Nature Communications, 14(1), 6674. https://doi.org/10.1038/s41467-023-42411-2
Parizi, E., Hosseini, S. M., Ataie-Ashtiani, B., & Simmons, C. T. (2020). Normalized difference vegetation index as the dominant predicting factor of groundwater recharge in phreatic aquifers: Case studies across Iran. Scientific Reports, 10(1), Article 17473. https://doi.org/10.1038/s41598-020-74561-4
Rahmati, O., Nazari Samani, A., Mahdavi, M., Pourghasemi, H. R., & Zeinivand, H. (2015). Groundwater potential mapping at Kurdistan region of Iran using analytic hierarchy process and GIS. Arabian Journal of Geosciences, 8(9), 7059–7071. https://doi.org/10.1007/s12517-014-1668-4
Razavi Khorasan Regional Water Company. (2026). Official website. Retrieved August 2, 2026, from https://khrw.ir
Saadatpour, A., Alizadeh, A., Ziaei, A. N., & Izady, A. (2019). Integrated surface and groundwater flow modeling in Neishaboor watershed with SWAT-MODFLOW. Journal of Water and Soil, 33(4), 521–536. https://doi.org/10.22067/jsw.v0i0.74658.[In Persian].
Schaap, M. G., Leij, F. J., & van Genuchten, M. T. (2001). Rosetta: A computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions. Journal of Hydrology, 251(3–4), 163–176. https://doi.org/10.1016/S0022-1694(01)00466-8
Souissi, D., Msaddek, M. H., Zouhri, L., Chenini, I., El May, M., & Dlala, M. (2018). Mapping groundwater recharge potential zones in arid region using GIS and Landsat approaches, southeast Tunisia. Hydrological Sciences Journal, 63(2), 251–268. https://doi.org/10.1080/02626667.2017.1414383
hajian, F. (2026). Hybrid Machine Learning–Analytic Hierarchy Process Model for Improving the Accuracy of Groundwater Recharge Potential Mapping in the Neyshabur Aquifer, Northeastern Iran. Journal of Aquifer and Qanat, 7(1), 253–272. https://doi.org/10.22077/jaaq.2026.11587.1146
MLA
hajian, F. "Hybrid Machine Learning–Analytic Hierarchy Process Model for Improving the Accuracy of Groundwater Recharge Potential Mapping in the Neyshabur Aquifer, Northeastern Iran", Journal of Aquifer and Qanat, 7, 1, 2026, 253–272. doi: 10.22077/jaaq.2026.11587.1146
HARVARD
hajian, F. (2026). 'Hybrid Machine Learning–Analytic Hierarchy Process Model for Improving the Accuracy of Groundwater Recharge Potential Mapping in the Neyshabur Aquifer, Northeastern Iran', Journal of Aquifer and Qanat, 7(1), pp. 253–272. doi: 10.22077/jaaq.2026.11587.1146
CHICAGO
hajian, F., "Hybrid Machine Learning–Analytic Hierarchy Process Model for Improving the Accuracy of Groundwater Recharge Potential Mapping in the Neyshabur Aquifer, Northeastern Iran." Journal of Aquifer and Qanat, 7 1 (2026): 253–272, doi: 10.22077/jaaq.2026.11587.1146
VANCOUVER
hajian F. Hybrid Machine Learning–Analytic Hierarchy Process Model for Improving the Accuracy of Groundwater Recharge Potential Mapping in the Neyshabur Aquifer, Northeastern Iran. Journal of Auifer and Qanat. 2026;7(1):253–272 (In Persian). doi: 10.22077/jaaq.2026.11587.1146
