Research Associate, at INRS – Institut national de la recherche scientifique, Canada.
Dr. Hadi Sanikhani is a dedicated environmental engineer and water resources specialist currently serving as a Visiting Researcher at INRS, Québec. He focuses his research on the hydrological impacts of climate change in cold regions, particularly runoff dynamics and flood risk. By integrating AI-enhanced models—such as SWAT, MODFLOW, HEC‑HMS, and HEC‑RAS—with high-resolution geospatial and CMIP climate projection data, Dr. Sanikhani aims to advance our understanding of flood hazards and improve predictive tools for extreme water events. He collaborates effectively within interdisciplinary teams and is passionate about bridging physical modeling with data-driven techniques to manage and protect vulnerable water systems in Canada and beyond.
Professional Profile
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🎓 Education
Dr. Sanikhani earned his Ph.D. in Water Resources Engineering (2010–2015) from the University of Tabriz, where his thesis focused on river flow prediction using nearest-neighbor and probabilistic ensemble approaches. Prior, he obtained an M.Sc. in Irrigation and Drainage (2005–2008) at the same university, investigating scour mitigation using rectangular collars around bridge piers. He completed his B.Sc. in Water Science and Engineering (2001–2005) from Mazandaran University, specializing in optimizing discharge–sediment relationships in the Gorganrood River. Throughout, he developed strong expertise in hydrological systems, AI modeling, and practical solutions for water infrastructure design.
💼 Experience
From September 2024 to present, Dr. Sanikhani conducts advanced climate-driven hydrological research at INRS Québec. Previously (2015–2024), he was a Water Resources Researcher at University of Kurdistan, Iran, where he modeled runoff, sediment transport, and climate impacts. In 2013, he served as a Visiting Researcher at both Delft University of Technology and Politecnico di Milano, applying high-end hydrological analysis in European environments. His roles have consistently centered on developing predictive models using remote sensing, AI, and ensemble techniques to support flood risk assessment and water system management across diverse climates.
🔬 Research Interests
Dr. Sanikhani’s research spans hydrological and hydraulic modeling (SWAT, HEC‑HMS/RAS, MODFLOW), flood hazard assessment, and urban stormwater systems. He investigates climate change impacts using CMIP datasets and high-resolution geospatial tools. His work leverages AI and machine learning—such as random forests, MLP, and genetic programming—for forecasting extreme hydrological events. He also explores groundwater–surface water interactions, remote sensing via Google Earth Engine, AI-assisted hydrology, and community-engaged research, aiming to integrate technological and social insights into sustainable water resource management.
🏆 Awards
Dr. Sanikhani has earned multiple honors in recognition of his scientific excellence. He received the “Distinguished Researcher” award from the University of Kurdistan (2018–2019) for outstanding scholarship in environmental modeling. Earlier, as a Ph.D. student at the University of Tabriz, he was a “Distinguished Student” (2010–2012) and held a prestigious Ph.D. scholarship funded by the Iranian Ministry of Science, Research and Technology (2010–2014). These accolades reflect his sustained contributions to hydrological science and academic leadership.
📄 Top Noted Publications
📘 Modeling wetted areas of moisture bulb for drip irrigation systems: An enhanced empirical model and artificial neural network
Authors: Karimi, B.; Mohammadi, P.; Sanikhani, H.; Salih, S. Q.; Yaseen, Z. M.
Journal: Computers and Electronics in Agriculture (Volume 178), November 2020, Article 105767.
DOI: 10.1016/j.compag.2020.105767
Abstract Summary: Developed ANN and nonlinear regression models to estimate vertical up/down wetted areas around drippers based on soil texture, discharge rate, depth, irrigation time, etc.—outperformed dimensional analysis models.
Scopus Citations: 48
📘 Integrative stochastic model standardization with genetic algorithm for rainfall pattern forecasting in tropical and semi-arid environments
Authors: Salih, S. Q.; Sharafati, A.; Ebtehaj, I.; Sanikhani, H.; Siddique, R.; Deo, R. C.; Bonakdari, H.; Shahid, S.; Yaseen, Z. M.
Journal: Hydrological Sciences Journal, Volume 65 Issue 7, pp. 1145–1157, May 18, 2020.
DOI: 10.1080/02626667.2020.1734813
Abstract Summary: Introduced a novel stochastic forecasting method combining seasonal differencing, standardization, spectral analysis, and genetic algorithms; achieved R² ≈ 0.80–0.94 (Malaysia) and 0.89–0.91 (Iraq) across multiple stations.
Scopus Citations: 45
📘 Novel approaches for air temperature prediction: A comparison of four hybrid evolutionary fuzzy models
Authors: [Likely] Azad, [et al.]
Journal: Meteorological Applications, 2020.
DOI: 10.1002/met.1817
Abstract Summary: Tested four hybrid evolutionary fuzzy models (ANFIS combined with GA, PSO, ACOR, and DE) to predict monthly minimum, mean, and maximum air temperatures across 34 stations in Iran. Found ANFIS–GA to consistently outperform others—e.g., reducing RMSE from 1.22 °C to 1.12 °C in Mashhad.
Citation Count: Not available (recommend checking Scopus/Google Scholar)
📘 Monthly long-term rainfall estimation in Central India using M5Tree, MARS, LSSVR, ANN and GEP models
Authors: Mirabbasi, R.; Kisi, O.; Sanikhani, H.; Gajbhiye Meshram, S.
Journal: Neural Computing and Applications, 2019; exact volume/issue TBD.
Abstract Summary: Compared five different data-driven models (M5Tree, MARS, LSSVR, ANN, GEP) for monthly rainfall estimation based on data from 61 stations in Madhya Pradesh and Chhattisgarh. LSSVR achieved the highest accuracy (RMSE ≈ 13.93 mm, MAE ≈ 9.52 mm, R² ≈ 0.995), whereas GEP performed worst (RMSE ≈ 36.74 mm)
Citation Count: Not listed (suggest using Google Scholar/Scopus)
Conclusion
Dr. Hadi Sanikhani is a strong and suitable candidate for the Best Researcher Award, especially in the domain of water resources, flood modeling, and climate change resilience. His interdisciplinary, AI-enhanced approach to environmental modeling and international research collaborations distinguish him as an impactful and forward-thinking researcher.