HEDDAM SALIM, HDR, Full Professor

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Ozgur KisiDepartment of Civil Engineering, University of Applied Sciences, Lübeck, GermanyPatvirtintas el. paštas th-luebeck.de
Sungwon KimProfessor, Dept. of Railroad Construction and Safety Engineering, Dongyang UniversityPatvirtintas el. paštas dyu.ac.kr
Rana Muhammad Adnan IkramResearch Professor, Guangzhou UniversityPatvirtintas el. paštas gzhu.edu.cn
Mohammad Zounemat-KermaniDepartment of Water Engineering, Shahid Bahonar University of Kerman, Kerman, IranPatvirtintas el. paštas uk.ac.ir
Lakhdar DjemilihydrauliquePatvirtintas el. paštas univ-annaba.dz
Larbi HouichiProfessor, University of Batna 2, AlgeriaPatvirtintas el. paštas univ-batna2.dz
Anurag MalikPunjab Agricultural UniversityPatvirtintas el. paštas pau.edu
Meysam AlizamirPhD, Civil engineering departmentPatvirtintas el. paštas iauh.ac.ir
Behrooz KeshtegarProfessor, University of ZabolPatvirtintas el. paštas uoz.ac.ir
Professor Ravinesh Deo, Highly Cited...Professor in Mathematics and Artificial IntelligencePatvirtintas el. paštas usq.edu.au
bermad AbdelMalekENPPatvirtintas el. paštas g.enp.edu.dz
Binquan LiHohai UniversityPatvirtintas el. paštas hhu.edu.cn
Hossein BonakdariAssociate ProfessorPatvirtintas el. paštas fsaa.ulaval.ca
Isa EbtehajResearch Assistant, Université LavalPatvirtintas el. paštas ulaval.ca
Andrea PETROSELLITuscia UniversityPatvirtintas el. paštas unitus.it
Marijana Hadzima-NyarkoFull Professor of Civil Engineering, University J.J.Strossmayer in Osijek, Faculty of CivilPatvirtintas el. paštas gfos.hr
Emmanuel Karlo NyarkoFaculty of Electrical Engineering, Computer Science and Information Technology, OsijekPatvirtintas el. paštas etfos.hr
Shun-Peng Zhu (朱顺鹏)Professor, University of Electronic Science and Technology of ChinaPatvirtintas el. paštas uestc.edu.cn
Sebastiano PiccolroazAssistant Professor, University of Trento, ItalyPatvirtintas el. paštas unitn.it
ACHOUR BACHIRProfesseur d'Hydraulique, Université de Biskra, AlgériePatvirtintas el. paštas univ-biskra.dz

Stebėti

HEDDAM SALIM, HDR, Full Professor

Faculty of Science, Agronomy Department, Hydraulic Division University 20 Août 1955 SKIKDA 21000

Patvirtintas el. paštas univ-skikda.dz - Pagrindinis puslapis

Artificial Neural Network ANN Multi-Layer Perceptron Neural Network MLPNN Generalized Regression Neural Networks GRNN Radial


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
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	270	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	269	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	183	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	141	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	123	2012
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	109	2021
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	108	2020
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	107	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	105	2021
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	105	2020
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	105	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 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	99	2018
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	98	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	92	2019
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	89	2016
Application of Water Quality Indices, Machine Learning Approaches, and GIS to Identify Groundwater Quality for Irrigation Purposes: A Case Study of Sahara Aquifer, Doucen Plain … H Gaagai, A., Aouissi, H. A., Bencedira, S., Hinge, G., Athamena, A., Heddam ... Water 15 (2), 289, 2023	85	2023
Pan evaporation estimation by relevance vector machine tuned with new metaheuristic algorithms using limited climatic data RM Adnan, RR Mostafa, HL Dai, S Heddam, A Kuriqi, O Kisi Engineering Applications of Computational Fluid Mechanics 17 (1), 2192258, 2023	78	2023
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	78	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	78	2019

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