| Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network P Taherei Ghazvinei, H Hassanpour Darvishi, A Mosavi, KW Yusof, ... Engineering Applications of Computational Fluid Mechanics 12 (1), 738-749, 2018 | 160 | 2018 |
| Extreme learning machine for prediction of heat load in district heating systems S Sajjadi, S Shamshirband, M Alizamir, L Yee, Z Mansor, AA Manaf, ... Energy and Buildings 122, 222-227, 2016 | 152 | 2016 |
| A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions M Alizamir, S Kim, O Kisi, M Zounemat-Kermani Energy 197, 117239, 2020 | 133 | 2020 |
| Modelling reference evapotranspiration using a new wavelet conjunction heuristic method: wavelet extreme learning machine vs wavelet neural networks O Kisi, M Alizamir Agricultural and Forest Meteorology 263, 41-48, 2018 | 129 | 2018 |
| Dissolved oxygen prediction using a new ensemble method O Kisi, M Alizamir, ARD Gorgij Environmental Science and Pollution Research, 1-15, 2020 | 108 | 2020 |
| Economic growth forecasting by artificial neural network with extreme learning machine based on trade, import and export parameters S Sokolov-Mladenović, M Milovančević, I Mladenović, M Alizamir Computers in Human Behavior 65, 43-45, 2016 | 95 | 2016 |
| Advanced machine learning model for better prediction accuracy of soil temperature at different depths M Alizamir, O Kisi, AN Ahmed, C Mert, CM Fai, S Kim, NW Kim, ... Plos one 15 (4), e0231055, 2020 | 93 | 2020 |
| Modelling long-term groundwater fluctuations by extreme learning machine using hydro-climatic data M Alizamir, O Kisi, M Zounemat-Kermani Hydrological sciences journal 63 (1), 63-73, 2018 | 93 | 2018 |
| Modelling reference evapotranspiration by combining neuro-fuzzy and evolutionary strategies M Alizamir, O Kisi, R Muhammad Adnan, A Kuriqi Acta Geophysica 68, 1113-1126, 2020 | 89 | 2020 |
| Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data O Kisi, M Alizamir, M Zounemat-Kermani Natural Hazards 87 (1), 367-381, 2017 | 73 | 2017 |
| On the complexities of sediment load modeling using integrative machine learning: Application of the great river of Loíza in Puerto Rico M Zounemat-Kermani, A Mahdavi-Meymand, M Alizamir, S Adarsh, ... Journal of hydrology 585, 124759, 2020 | 65 | 2020 |
| Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction M Ehteram, AN Ahmed, SD Latif, YF Huang, M Alizamir, O Kisi, C Mert, ... Environmental Science and Pollution Research 28 (2), 1596-1611, 2021 | 62 | 2021 |
| Assessing the biochemical oxygen demand using neural networks and ensemble tree approaches in South Korea S Kim, M Alizamir, M Zounemat-Kermani, O Kisi, VP Singh Journal of Environmental Management 270, 110834, 2020 | 52 | 2020 |
| Improving the accuracy of daily solar radiation prediction by climatic data using an efficient hybrid deep learning model: Long short-term memory (LSTM) network coupled with … M Alizamir, J Shiri, AF Fard, S Kim, ARD Gorgij, S Heddam, VP Singh Engineering Applications of Artificial Intelligence 123, 106199, 2023 | 49 | 2023 |
| On the implementation of a novel data-intelligence model based on extreme learning machine optimized by bat algorithm for estimating daily chlorophyll-a concentration: case … M Alizamir, S Heddam, S Kim, AD Mehr Journal of Cleaner Production 285, 124868, 2021 | 46 | 2021 |
| Forecasting accident frequency of an urban road network: A comparison of four artificial neural network techniques H Behbahani, AM Amiri, R Imaninasab, M Alizamir Journal of Forecasting 37 (7), 767-780, 2018 | 40 | 2018 |
| Modelling daily soil temperature by hydro-meteorological data at different depths using a novel data-intelligence model: deep echo state network model M Alizamir, S Kim, M Zounemat-Kermani, S Heddam, AH Shahrabadi, ... Artificial Intelligence Review, 1-28, 2020 | 37 | 2020 |
| Deep echo state network: a novel machine learning approach to model dew point temperature using meteorological variables M Alizamir, S Kim, O Kisi, M Zounemat-Kermani Hydrological Sciences Journal 65 (7), 1173-1190, 2020 | 36 | 2020 |
| Drought modelling by standard precipitation index (SPI) in a semi-arid climate using deep learning method: long short-term memory A Docheshmeh Gorgij, M Alizamir, O Kisi, A Elshafie Neural Computing and Applications, 1-18, 2021 | 34 | 2021 |
| Solar radiation estimation in Mediterranean climate by weather variables using a novel Bayesian model averaging and machine learning methods O Kisi, M Alizamir, S Trajkovic, J Shiri, S Kim Neural Processing Letters 52 (3), 2297-2318, 2020 | 34 | 2020 |
Ozgur KisiDepartment of Civil Engineering, University of Applied Sciences, Lübeck, GermanyBestätigte E-Mail-Adresse bei th-luebeck.de
