| Prediction of millers ferry dam reservoir level in USA using artificial neural network F Üneş, M Demirci, Ö Kişi Periodica Polytechnica Civil Engineering 59 (3), 309-318, 2015 | 66 | 2015 |
| Forecasting of Suspended Sediment in Rivers Using Artificial Neural Networks Approach B Taşar, YZ Kaya, H Varçin, F Üneş, M Demirci International Journal of Advanced Engineering Research and Science 4 (Issue-12), 2017 | 46 | 2017 |
| Estimating dam reservoir level fluctuations using data-driven techniques F Üneş, M Demirci, B Taşar, YZ Kaya, H Varçin Polish Journal of Environmental Studies, 2019 | 42 | 2019 |
| Groundwater level prediction using artificial neural network and M5 tree models YZ Kaya, F Üneş, M Demirci, B Taşar, H Varçin Aerul si Apa. Componente ale Mediului, 195-201, 2018 | 41 | 2018 |
| Estimation of daily evapotranspiration in Košice City (Slovakia) using several soft computing techniques YZ Kaya, M Zelenakova, F Üneş, M Demirci, H Hlavata, P Mesaros Theoretical and Applied Climatology 144, 287-298, 2021 | 35 | 2021 |
| Prediction of cross-shore sandbar volumes using neural network approach M Demirci, F Üneş, MS Aköz Journal of Marine Science and Technology 20, 171-179, 2015 | 35 | 2015 |
| Yapay sinir ağları yöntemi kullanılarak buharlaşma miktarı tahmini B Taşar, F Üneş, M Demirci, YZ Kaya DÜMF Mühendislik Dergisi 9 (1), 543-551, 2018 | 34 | 2018 |
| Estimation of groundwater level using artificial neural networks: a case study of Hatay-Turkey F Üneş, M Demirci, E İspir, YZ Kaya, M Mamak, B Taşar Vilnius Gediminas Technical University Publishing House" Technika", 2017 | 33 | 2017 |
| Estimating the energy production of the wind turbine using artificial neural network İ Mert, C Karakuş, F Üneş Neural Computing and Applications 27, 1231–1244., 2016 | 32 | 2016 |
| Prediction of density flow plunging depth in dam reservoirs: an artificial neural network approach F Ünes Clean–Soil, Air, Water 38 (3), 296-308, 2010 | 31 | 2010 |
| Daily reference evapotranspiration prediction based on climatic conditions applying different data mining techniques and empirical equations F Üneş, YZ Kaya, M Mamak Theoretical and Applied Climatology 141, 763-773, 2020 | 28 | 2020 |
| Modeling of groundwater level using artificial intelligence techniques: A case study of Reyhanli region in Turkey M Demirci, F Üneş, S Körlü APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH, 2019 | 26 | 2019 |
| MODELING OF DAM RESERVOIR VOLUME USING ADAPTIVE NEURO FUZZY METHOD. M Demirci, F Unes, YZ Kaya, B Tasar, H Varcin Air & Water Components of the Environment/Aerul si Apa Componente ale Mediului, 2018 | 26 | 2018 |
| Suspended sediment estimation using an artificial intelligence approach M Demirci, F Üneş, S Saydemir Sediment matters, 83-95, 2015 | 26 | 2015 |
| A comparative study of estimating solar radiation using machine learning approaches: DL, SMGRT, and ANFIS İ Üstün, F Üneş, İ Mert, C Karakuş Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 1-24, 2022 | 25 | 2022 |
| Modeling of dam reservoir volume using generalized regression neural network, support vector machines and M5 decision tree models F Unes, M Demirci, B Tasar, YZ Kaya, H Varçin APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH 17 (3), 7043-7055, 2019 | 24 | 2019 |
| The Evaluation and Comparison of Daily Reference Evapotranspiration with ANN and Empirical Methods F Üneş, S Doğan, B Taşar, YZ Kaya, M Demirci Natural and Engineering Sciences, 2018 | 24 | 2018 |
| Prediction of Dam Reservoir Volume Fluctuations Using Adaptive Neuro Fuzzy Approach F Üneş, F Gumuscan, M Demirci European Journal of Engineering and Natural Sciences 2 (Issue 1), pp. 144-148, 2017 | 23 | 2017 |
| Dam reservoir level modeling by neural network approach: A case study F Ünes Neural Network World 20 (4), 461, 2010 | 22 | 2010 |
| River flow estimation using artificial intelligence and fuzzy techniques F Üneş, M Demirci, M Zelenakova, M Çalışıcı, B Taşar, F Vranay, YZ Kaya Water 12 (9), 2427, 2020 | 19 | 2020 |
Mustafa DemirciIskenderun Technical University, Civil EngineeringVerified email at iste.edu.tr
