مقایسه مدل‌های عددی و الگوریتم‌های یادگیری ماشین با رویکرد پیش‌بینی تراز آب زیرزمینی: مطالعه‌ای موردی در دشت بیرجند

Adombi, A. V. D. P., Chesnaux, R., Boucher, M.-A., Braun, M., & Lavoie, J. (2024). A causal physics-informed deep learning formulation for groundwater flow modeling and climate change effect analysis. Journal of Hydrology, 637, 131370. https://doi.org/10.1016/j.jhydrol.2024.131370

Aghlmand, R., & Abbasi, A. (2019). Application of MODFLOW with Boundary Conditions Analyses Based on Limited Available Observations: A Case Study of Birjand Plain in East Iran. Water, 11(9), 1904. https://doi.org/10.3390/w11091904

Alao, J. O., Bello, A., Lawal, H., & Abdullahi, D. (2024). Assessment of groundwater challenge and the sustainable management strategies. Results in Earth Sciences, 2, 100049. https://doi.org/10.1016/j.rines.2024.100049

Boo, K.B.W., El-Shafie, A., Othman, F., Khan, M.M.H., Birima, A.H., Ahmed, A.N., 2024. Groundwater level forecasting with machine learning models: A review. Water Research 252, 121249.DOI: https://doi.org/10.1016/j.watres.2024.121249

Borzì, I. (2025). Modeling Groundwater Resources in Data-Scarce Regions for Sustainable Management: Methodologies and Limits. Hydrology, 12(1), 11. https://doi.org/10.3390/hydrology12010011

Chang, Y.-W., Sun, W., Kow, P.-Y., Lee, M.-H., Chang, L.-C., & Chang, F.-J. (2025). Advanced groundwater level forecasting with hybrid deep learning model: Tackling water challenges in Taiwan’s largest alluvial fan. Journal of Hydrology, 655, 132887. https://doi.org/10.1016/j.jhydrol.2025.132887

Chen, C., Zhang, H., Shi, W., Zhang, W., & Xue, Y. (2023). A novel paradigm for integrating physics-based numerical and machine learning models: A case study of eco-hydrological model. Environmental Modelling & Software, 163, 105669. https://doi.org/10.1016/j.envsoft.2023.105669

Cui, X., Wang, Z., Xu, N., Wu, J., & Yao, Z. (2024). A secondary modal decomposition ensemble deep learning model for groundwater level prediction using multi-data. Environmental Modelling & Software, 175, 105969. https://doi.org/10.1016/j.envsoft.2024.105969

Di Salvo, C. (2022). Improving Results of Existing Groundwater Numerical Models Using Machine Learning Techniques: A Review. Water, 14(15), 2307. https://doi.org/10.3390/w14152307

Dunnington, D. W., Trueman, B. F., Raseman, W. J., Anderson, L. E., & Gagnon, G. A. (2021). Comparing the Predictive Performance, Interpretability, and Accessibility of Machine Learning and Physically Based Models for Water Treatment. ACS ES&T Engineering, 1(3), 348–356. https://doi.org/10.1021/acsestengg.0c00053

Ehteram, M., & Ghanbari-Adivi, E. (2023). Self-attention (SA) temporal convolutional network (SATCN)-long short-term memory neural network (SATCN-LSTM): An advanced python code for predicting groundwater level. Environmental Science and Pollution Research, 30(40), 92903–92921. https://doi.org/10.1007/s11356-023-28771-8

Ghebrehiwot, A. A., & Kozlov, D. V. (2019). Hydrological modelling for ungauged basins of arid and semi-arid regions: Review. Vestnik MGSU, 8, 1023–1036. https://doi.org/10.22227/1997-0935.2019.8.1023-1036

Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology, 377(1-2), 80–91. https://doi.org/10.1016/j.jhydrol.2009.08.003

Igwebuike, N., Ajayi, M., Okolie, C., Kanyerere, T., & Halihan, T. (2025). Application of machine learning and deep learning for predicting groundwater levels in the West Coast Aquifer System, South Africa. Earth Science Informatics, 18(1), 6. https://doi.org/10.1007/s12145-024-01623-w

Janssen, J., Tootchi, A., & Ameli, A. A. (2025). Tackling water table depth modeling via machine learning: From proxy observations to verifiability. Advances in Water Resources, 201, 104955. https://doi.org/10.1016/j.advwatres.2025.104955

Khan, J., Lee, E., Balobaid, A.S., Kim, K., 2023. A Comprehensive Review of Conventional, Machine Learning, and Deep Learning Models for Groundwater Level (GWL) Forecasting. Applied Sciences 13(4), 2743.DOI: https://doi.org/10.3390/app13042743

Navarro-Farfán, M. D. M., García-Romero, L., Martínez-Cinco, M. A., Hernández-Hernández, M. A., & Sánchez-Quispe, S. T. (2024). Comparison between MODFLOW Groundwater Modeling with Traditional and Distributed Recharge. Hydrology, 11(1), 9. https://doi.org/10.3390/hydrology11010009

Porhemmat, J., Sedghi, H., Babazadeh, H., & Fotovat, M. (2019). Evaluation of WEAP-MODFLOW model as integrated water resources management model for sustainable development. Civil Engineering Infrastructures Journal, Online First. https://doi.org/10.22059/ceij.2019.260084.1495

Pourmorad, S., Kabolizade, M., & Dimuccio, L. A. (2024). Artificial Intelligence Advancements for Accurate Groundwater Level Modelling: An Updated Synthesis and Review. Applied Sciences, 14(16), 7358. https://doi.org/10.3390/app14167358

Rad, M., Abtahi, A., Berndtsson, R., McKnight, U. S., & Aminifar, A. (2024). Interpretable machine learning for predicting the fate and transport of pentachlorophenol in groundwater. Environmental Pollution, 345, 123449. https://doi.org/10.1016/j.envpol.2024.123449

Rammohan, B., Partheeban, P., Ranganathan, R., & Balaraman, S. (2024). Groundwater Quality Prediction and Analysis Using Machine Learning Models and Geospatial Technology. Sustainability, 16(22), 9848. https://doi.org/10.3390/su16229848

Sheikh Khozani, Z., Barzegari Banadkooki, F., Ehteram, M., Najah Ahmed, A., & El-Shafie, A. (2022). Combining autoregressive integrated moving average with Long Short-Term Memory neural network and optimisation algorithms for predicting ground water level. Journal of Cleaner Production, 348, 131224. https://doi.org/10.1016/j.jclepro.2022.131224

Torres-Martínez, J. A., Mahlknecht, J., Kumar, M., Loge, F. J., & Kaown, D. (2024). Advancing groundwater quality predictions: Machine learning challenges and solutions. Science of The Total Environment, 949, 174973. https://doi.org/10.1016/j.scitotenv.2024.174973

Zarei, E., Saleh, F. N., & Dalir, A. N. (2024). Comparing the hybrid-lumped-LSTM model with a semi-distributed model for improved hydrological modeling. Journal of Water and Climate Change, 15(8), 4099–4113. https://doi.org/10.2166/wcc.2024.269