| Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs RM Adnan, Z Liang, S Heddam, M Zounemat-Kermani, O Kisi, B Li Journal of Hydrology 586, 124371, 2020 | 244 | 2020 |
| Groundwater level prediction using machine learning models: A comprehensive review ZS Hai Tao, Mohammed Majeed Hameed, Haydar Abdulameer Marhoon, Mohammad ... Neurocomputing 489, 271-308, 2022 | 212 | 2022 |
| Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and M5 model tree S Heddam, O Kisi Journal of Hydrology 559, 499-509, 2018 | 180 | 2018 |
| Rainfall pattern forecasting using novel hybrid intelligent model based ANFIS-FFA ZM Yaseen, MI Ghareb, I Ebtehaj, H Bonakdari, R Siddique, S Heddam, ... Water resources management 32, 105-122, 2018 | 137 | 2018 |
| ANFIS-based modelling for coagulant dosage in drinking water treatment plant: a case study S Heddam, A Bermad, N Dechemi Environmental monitoring and assessment 184, 1953-1971, 2012 | 117 | 2012 |
| Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors S Heddam, O Kisi Environmental Science and Pollution Research 24 (20), 16702-16724, 2017 | 104 | 2017 |
| Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models S Zhu, S Heddam, EK Nyarko, M Hadzima-Nyarko, S Piccolroaz, S Wu Environmental Science and Pollution Research 26, 402-420, 2019 | 101 | 2019 |
| Modeling daily reference evapotranspiration (ET0) in the north of Algeria using generalized regression neural networks (GRNN) and radial basis function neural … I Ladlani, L Houichi, L Djemili, S Heddam, K Belouz Meteorology and Atmospheric Physics 118, 163-178, 2012 | 101 | 2012 |
| River water salinity prediction using hybrid machine learning models AM Melesse, K Khosravi, JP Tiefenbacher, S Heddam, S Kim, A Mosavi, ... Water 12 (10), 2951, 2020 | 98 | 2020 |
| Modelling of daily lake surface water temperature from air temperature: Extremely randomized trees (ERT) versus Air2Water, MARS, M5Tree, RF and MLPNN S Heddam, M Ptak, S Zhu Journal of Hydrology 588, 125130, 2020 | 98 | 2020 |
| Modeling daily dissolved oxygen concentration using modified response surface method and artificial neural network: a comparative study B Keshtegar, S Heddam Neural Computing and Applications 30 (10), 2995-3006, 2018 | 98 | 2018 |
| Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model RM Adnan, A Petroselli, S Heddam, CAG Santos, O Kisi Stochastic Environmental Research and Risk Assessment 35 (3), 597-616, 2021 | 92 | 2021 |
| The implementation of univariable scheme-based air temperature for solar radiation prediction: New development of dynamic evolving neural-fuzzy inference system model O Kisi, S Heddam, ZM Yaseen Applied Energy 241, 184-195, 2019 | 90 | 2019 |
| Prediction of dissolved oxygen in urban rivers at the Three Gorges Reservoir, China: extreme learning machines (ELM) versus artificial neural network (ANN) S Zhu, S Heddam Water Quality Research Journal, 2019 | 88 | 2019 |
| Estimating reference evapotranspiration using hybrid adaptive fuzzy inferencing coupled with heuristic algorithms RM Adnan, RR Mostafa, ARMT Islam, O Kisi, A Kuriqi, S Heddam Computers and Electronics in Agriculture 191, 106541, 2021 | 87 | 2021 |
| Predicting effluent biochemical oxygen demand in a wastewater treatment plant using generalized regression neural network based approach: a comparative study S Heddam, H Lamda, S Filali Environmental Processes 3, 153-165, 2016 | 83 | 2016 |
| Applications of radial-basis function and generalized regression neural networks for modeling of coagulant dosage in a drinking water-treatment plant: comparative study S Heddam, A Bermad, N Dechemi Journal of Environmental Engineering 137 (12), 1209-1214, 2011 | 75 | 2011 |
| Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach RM Adnan, A Petroselli, S Heddam, CAG Santos, O Kisi Natural Hazards 105, 2987-3011, 2021 | 72 | 2021 |
| Extreme learning machine-based prediction of daily water temperature for rivers S Zhu, S Heddam, S Wu, J Dai, B Jia Environmental Earth Sciences 78, 1-17, 2019 | 69 | 2019 |
| Modeling hourly dissolved oxygen concentration (DO) using two different adaptive neuro-fuzzy inference systems (ANFIS): a comparative study S Heddam Environmental Monitoring and Assessment 186 (1), 597-619, 2014 | 69 | 2014 |
Ozgur KisiDepartment of Civil Engineering, University of Applied Sciences, Lübeck, GermanyEmail được xác minh tại th-luebeck.de
