HEDDAM SALIM, HDR, Full Professor
HEDDAM SALIM, HDR, Full Professor
Faculty of Science, Agronomy Department, Hydraulic Division University 20 Août 1955 SKIKDA 21000
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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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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