Department of Architecture and Civil Engineering, University of Applied Sciences, Lübeck, Germany;
Department of Civil Engineering, The School of Business, Technology and Education, Ilia State University, Tbilisi, Georgia
PhD, Professor

Scopus profile: link
Researcher ID: AAD-8932-2019
Google Scholar profile: link
ID ORCID: https://orcid.org/0000-0001-7847-5872

Selected Publications:

1. Kim, S., Seo, Y., Malik, A., Kim, S., Heddam, S., Yaseen, Z. M., Kisi, O., Singh, V. P. (2023). Quantification of river total phosphorus using integrative artificial intelligence models. Ecological Indicators, 153, 110437. doi: https://doi.org/10.1016/j.ecolind.2023.110437 
   
   
2. Van Thieu, N., Deb Barma, S., Van Lam, T., Kisi, O., Mahesha, A. (2023). Groundwater level modeling using Augmented Artificial Ecosystem Optimization. Journal of Hydrology, 617, 129034. doi: https://doi.org/10.1016/j.jhydrol.2022.129034 
   
   
3. Adnan, R. M., Dai, H.-L., Mostafa, R. R., Islam, A. R. Md. T., Kisi, O., Heddam, S., Zounemat-Kermani, M. (2022). Modelling groundwater level fluctuations by ELM merged advanced metaheuristic algorithms using hydroclimatic data. Geocarto International, 38 (1). doi: https://doi.org/10.1080/10106049.2022.2158951 
   
   
4. Ikram, R. M. A., Hazarika, B. B., Gupta, D., Heddam, S., Kisi, O. (2022). Streamflow prediction in mountainous region using new machine learning and data preprocessing methods: a case study. Neural Computing and Applications. doi: https://doi.org/10.1007/s00521-022-08163-8 
   
   
5. Difi, S., Elmeddahi, Y., Hebal, A., Singh, V. P., Heddam, S., Kim, S., Kisi, O. (2022). Monthly streamflow prediction using hybrid extreme learning machine optimized by bat algorithm: a case study of Cheliff watershed, Algeria. Hydrological Sciences Journal, 68 (2), 189–208. doi: https://doi.org/10.1080/02626667.2022.2149334 
   
   
6. Samani, S., Vadiati, M., Nejatijahromi, Z., Etebari, B., Kisi, O. (2022). Groundwater level response identification by hybrid wavelet–machine learning conjunction models using meteorological data. Environmental Science and Pollution Research, 30 (9), 22863–22884. doi: https://doi.org/10.1007/s11356-022-23686-2 
   
   
7. Adnan, R. M., Mostafa, R. R., Dai, H.-L., Heddam, S., Kuriqi, A., Kisi, O. (2023). Pan evaporation estimation by relevance vector machine tuned with new metaheuristic algorithms using limited climatic data. Engineering Applications of Computational Fluid Mechanics, 17 (1). doi: https://doi.org/10.1080/19942060.2023.2192258 
   
   
8. Karbasi, M., Jamei, M., Malik, A., Kisi, O., Yaseen, Z. M. (2023). Multi-steps drought forecasting in arid and humid climate environments: Development of integrative machine learning model. Agricultural Water Management, 281, 108210. doi: https://doi.org/10.1016/j.agwat.2023.108210 
   
   
9. Ikram, R. M. A., Ewees, A. A., Parmar, K. S., Yaseen, Z. M., Shahid, S., Kisi, O. (2022). The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction. Applied Soft Computing, 131, 109739. doi: https://doi.org/10.1016/j.asoc.2022.109739 
   
   
10. Samani, S., Vadiati, M., Azizi, F., Zamani, E., Kisi, O. (2022). Groundwater Level Simulation Using Soft Computing Methods with Emphasis on Major Meteorological Components. Water Resources Management, 36 (10), 3627–3647. doi: https://doi.org/10.1007/s11269-022-03217-x 
    
    
11. Mirboluki, A., Mehraein, M., Kisi, O. (2022). Improving accuracy of neuro fuzzy and support vector regression for drought modelling using grey wolf optimization. Hydrological Sciences Journal, 67 (10), 1582–1597. doi: https://doi.org/10.1080/02626667.2022.2082877 
    
    
12. Kisi, O., Mirboluki, A., Naganna, S. R., Malik, A., Kuriqi, A., Mehraein, M. (2022). Comparative evaluation of deep learning and machine learning in modelling pan evaporation using limited inputs. Hydrological Sciences Journal, 67 (9), 1309–1327. doi: https://doi.org/10.1080/02626667.2022.2063724