Water Sciences and Hydroinformatics Research CenterAboutMissionPublicationsMembersNewsContacts

Publications

2026 Articles

  1. Samadianfard, S., & Gündüz, O. (2026). Decoding cross-correlated hydrochemical drivers for robust nutrient concentration prediction. Water, Air, & Soil Pollution, 237(951). https://doi.org/10.1007/s11270-026-09630-x
  2. Samadianfard, S., Aras, E., Çelik, D. Y., Sattari, M. T., & Gündüz, O. (2026). Hybrid CEEMDAN–GRU framework optimized with an MOOTLBO enables accurate real-time prediction of chemical oxygen demand (COD) in wastewater treatment. Environmental Science: Water Research & Technology. https://doi.org/10.1039/D6EW00014B
  3. Safari, S., Sharafati, A., Mosaferi, M., et al. (2026). Alternative urban drinking water supply scenarios under climate change: Evaluation of carbon footprint and energy demands. Journal of Environmental Health Science and Engineering, 24, 10. https://doi.org/10.1007/s40201-026-00977-
  4. Monavvar Sabegh, S., Zarehaghi, D., Samadianfard, S., Sattari, M. T., & Ahmad, S. (2026). Enhanced Pedotransfer Functions Through Optuna-Optimized Extreme Gradient Boosting: Application to Soil Water Retention Modeling. Earth, 7(3), 94. https://doi.org/10.3390/earth7030094
  5. Ghayurdoost, F., Zarghami, M., Sadeghfam, S., Jabraili-Andaryan, N., Nikmaram, S., Baba, A., Asgari Lajayer, B., & Mosaferi, M. (2026). Hydrogeochemical assessment and health risks of groundwater in Sahand volcanic foreland (NW Iran): Arsenic speciation and heavy metal risk indicators. Ecotoxicology and Environmental Safety, 310, Article 119746. https://doi.org/10.1016/j.ecoenv.2026.119746
  6. Feizi, H., & Sattari, M. T. (2026). Streamflow forecasting based on PatchTST, LSTM, and ensemble learning approaches. Water Resources Management, 40, 44. https://doi.org/10.1007/s11269-025-04397-y
  7. Seifian, Z., Hooshyaripor, F., Saghafian, B., & Mirabbasi, R. (2026). Investigating meteorological drought propagation to soil moisture drought: Insights from Iran’s diverse climate regions. Theoretical and Applied Climatology, 157, 15. https://doi.org/10.1007/s00704-025-05924-y
  8. Roushangar, K., & Panahi, A. (2026). Development of hybrid metaheuristic-kernel based models for accurate discharge coefficient prediction in side weirs with various geometries. Flow Measurement and Instrumentation, 107, Article 103092. https://doi.org/10.1016/j.flowmeasinst.2025.103092
  9. Monavvar Sabegh, S., Zarehaghi, D., & Samadianfard, S. (2026). Enhancing reference evapotranspiration prediction with biological ensemble support vector regression and MODIS data integration. Sustainable Water Resources Management,  12, 5. https://doi.org/10.1007/s40899-025-01317-1
  10. Abdi, E., Sattari, M. T., Samadianfard, S., & Ahmad, S. (2026). Decomposition–quantum hybrid model for accurate reservoir inflow prediction: A case study on Khoda Afarin Dam. Earth, 7(2), 35. https://doi.org/10.3390/earth7020035
  11. Allahverdipour, P., & Fakheri-Fard, A. (2026). Copula-based mapping of compound climate risks to rainfed wheat yield in semi-arid Iran. Earth Systems and Environment. Advance online publication. https://doi.org/10.1007/s41748-026-01080-z
  12. Roushangar, K., Shahnazi, S., & Hashemi, H. (2026). Groundwater artificial recharge with unconventional water: Global opportunities and challenges. In Hydrosystem Restoration Handbook(Vol. 4, pp. 199-224). Elsevier. https://doi.org/10.1016/B978-0-443-29811-0.00023-6
  13. Shahnazi, S., Roushangar, K., Farshbaf, A., et al. (2026). A novel implementation of a decomposition-enhanced hybrid GWO–KELM model with LUBE for constructing prediction intervals of groundwater drought. Earth Science Informatics, *19*, 31. https://doi.org/10.1007/s12145-026-02093-y

2025 Articles (Older)

  1. Feizi, H., Sattari, M. T., & Milewski, A. (2025). Improving stage-discharge relationship modeling accuracy using a hybrid ViT-CNN framework. Scientific Reports, *15*, 38031. https://doi.org/10.1038/s41598-025-21926-2
  2. Talebi, H., Citakoglu, H., Samadianfard, S., et al. (2025). Advanced hybrid machine learning for precise short-term drought prediction: A comparative study of SPI and SPEI indices in Iran's arid and semi-arid regions. Pure and Applied Geophysics. Advance online publication. https://doi.org/10.1007/s00024-025-03876-y
  3. Abdi, E., Sattari, M. T., Samadianfard, S., & Ahmad, S. (2025). Advancing hydrological prediction with hybrid quantum neural networks: A comparative study for Mile Mughan Dam. Water, *17*(24), 3592. https://doi.org/10.3390/w17243592

 


Copyright © Khazar University, 2026