- არგუსი
- ელ. ფოსტა
- კონტაქტი
Direction:Civil Engineering
Position:Full Professor
Dr. Kisi has been working as a Postdoctoral Researcher at the University of Applied Sciences Lübeck, Germany, since 2021. He previously held prominent academic positions, including Professor at Ilia State University, Georgia (2017–2021), and Dean of the Faculty of Architecture and Engineering at Basari University, Turkey (2012–2016). He also served as Head of the Civil Engineering Department at Erciyes University (2011–2012).
He received his Ph.D. in Hydraulic Engineering from Istanbul Technical University, Turkey, in 2003. Over the years, Dr. Kisi has gained recognition for his innovative contributions to hydrology, hydroinformatics, and environmental sciences. His research research primarily focuses on advancing water resources engineering through the development of innovative modeling and prediction techniques. This includes suspended sediment modeling, streamflow, flood and drought modeling, and novel trend analysis methods. He employs advanced statistical and machine learning (ML) approaches, including explainable ML and deep learning, combined with remote sensing data, empirical mode decomposition, and wavelet analysis. Additionally, He explores spatial and temporal hydroclimatic patterns using cutting-edge graphical trend methods, such as innovative trend analysis, innovative polygon trend analysis and wavelet analysis. He also integrate wavelet analysis, remote sensing, and machine learning with metaheuristic optimization techniques to address global challenges in these domains.
Dr. Kisi has been a recipient of prestigious awards such as the 2006 International Tison Award (Link 1) by the International Association of Hydrological Sciences (IAHS) and the Best Discussion Award by the Environmental and Water Resources Institute of the American Society of Civil Engineers (ASCE) in 2023. He was recognized as a Highly Cited Researcher by Clarivate in 2021 and consistently ranked among the Top 2% of Scientists Worldwide (Link 2) by Stanford University (2020–2025).
With over 600 peer-reviewed articles, 15 book chapters, and 30 discussions, Dr. Kisi’s research impact is evidenced by an h-index of 91 (Web of Science), 113 (Google Scholar), and 99 (Scopus).
In addition to his research achievements, Dr. Kisi has played a pivotal role in the academic publishing landscape. He is an Editorial Board Member of several prestigious journals, including:
Furthermore, he has served as a reviewer for over 100 journals indexed in the Science Citation Index (SCI), covering fields such as hydrology, irrigation, water resources, and hydro-informatics.
Dr. Kisi is also a full member of the Turkish Academy of Sciences (Link 3) since 2012, reflecting his enduring contributions to academia and scientific innovation.
Links:
His research primarily focuses on advancing water resources engineering through the development of innovative modeling and prediction techniques. This includes suspended sediment, streamflow, groundwater, water quality, flood and drought modeling, and novel trend analysis methods. He employs advanced statistical and machine learning (ML) approaches, including explainable ML and deep learning, combined with remote sensing data, empirical mode decomposition, and wavelet analysis. Additionally, he explores spatial and temporal hydroclimatic patterns using cutting-edge graphical trend methods, such as innovative trend analysis, innovative polygon trend analysis and wavelet analysis. He also integrates wavelet analysis, remote sensing, and machine learning with metaheuristic optimization techniques to address global challenges in these domains.
Publications
Samani, S., Vadiati, M., Kisi, O. 2025. Predicting Groundwater Levels in Coastal Aquifers Using Deep Learning Models: A Comparative Study of Sedimentary and Metamorphic Aquifers in Nova Scotia, Earth Science Informatics, 18, 389. https://doi.org/10.1007/s12145-025-01856-3.Kisi, O., Heddam, S., Parmar, K., Petroselli, A., Kulls, C., Zounemat-Kermani, M. 2025. Integrating Gaussian Process Regression with K-means Clustering for Enhanced Short-Term Rainfall-Runoff Modeling, Scientific Reports, 15, 7444. https://doi.org/10.1038/s41598-025-91339-8.Tosan, M., Nourani, V., Kisi, O., Dastourani, M. 2025. Evolution of ensemble machine learning approaches in water resources management: a review, Earth Science Informatics, 18, 416. https://doi.org/10.1007/s12145-025-01911-z.Shabbir, M., Chand, S., Iqbal, F., Kisi, O. 2025. A Novel Hybrid Approach for River Inflow Modelling: Case Study of Indus River Basin, Pakistan, ASCE J. of Hydrologic Eng., 30(3): 04025006. https://doi.org/10.1061/JHYEFF.HEENG-6368.
Piri, J., Kisi, O. (2024). Hybrid nonlinear probabilistic model using Monte Carlo simulation and hybrid Support Vector Regression for evaporation predictions, Hydrological Sciences Journal, 69(15), 2249–2277. https://doi.org/10.1080/02626667.2024.2403718.
Jannatkhah, M., Davarpanah, R., Fakouri, B., Kisi, O. (2024). Evaluation of total dissolved solids in rivers by improved neuro fuzzy approaches using metaheuristic algorithms, Earth Science Informatics, 1501–1522. https://doi.org/10.1007/s12145-024-01220-x.
Seyedian, S.M., Kisi, O., Parsaie, A., Kashani, M. (2024). Improving the Reliability of Compound Channel Discharge Prediction Using Machine Learning Techniques and Resampling Methods, Water Resources Management, 38, 4685–4709. https://doi.org/10.1007/s11269-024-03883-z.
Morovati, R., Kisi, O. 2024. Utilizing Hybrid Machine Learning Techniques and Gridded Precipitation Data for Advanced Discharge Simulation in Under-Monitored River Basins, Hydrology, 11(4), 48. https://doi.org/10.3390/hydrology11040048.
Idowu, M., Kulls, C., Kisi, O. (2024). A new method for monthly streamflow prediction using multi-source data: Range Dependent Multivariate Adaptive Regression Splines-Genetic Algorithm, Hydrological Sciences Journal, 69(13), 1860–1880. https://doi.org/10.1080/02626667.2024.2394639.
Shabbir, M., Chand, S., Iqbal, F., Kisi, O. (2024). Hybrid Approach for Streamflow Prediction: LASSO-Hampel Filter Integration with Support Vector Machines, Artificial Neural Networks, and Autoregressive Distributed Lag Models, Water Resources Management, 38, 4179–4196. https://doi.org/10.1007/s11269-024-03858-0.
Mahtabi, G., Kisi, O., Mozaffari, S., Taran, F. (2024). Predictive Modeling of Daily Precipitation Occurrence Using Weather Data of Prior Days in Various Climates, Earth Science Informatics, 17, 2381–2397. https://doi.org/10.1007/s12145-024-01289-4.
Kisi, O., Heddam, S., Parmar, K.S., Yaseen, Z.M., Kulls, C. (2024) Improved monthly streamflow prediction using integrated multivariate adaptive regression spline with K-means clustering: implementation of reanalyzed remote sensing data. Stoch Environ Res Risk Assess. 38, 2489–2519. https://doi.org/10.1007/s00477-024-02692-5.
Seyedian, S.M., Kisi, O. 2024. Uncertainty analysis of discharge coefficient predicted for rectangular side weir using machine learning methods, Journal of Hydrology and Hydromechanics, 72, 1, 113–130. https://doi.org/10.2478/johh-2023-0043.
Extensive list of publications
