HEDDAM SALIM, HDR, Full Professor
HEDDAM SALIM, HDR, Full Professor
Faculty of Science, Agronomy Department, Hydraulic Division University 20 Août 1955 SKIKDA 21000
Verified email at univ-skikda.dz - Homepage
Title
Cited by
Cited by
Year
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 (4), 1953-1971, 2012
672012
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
662018
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 (1), 105-122, 2018
592018
Modeling daily reference evapotranspiration (ET 0) in the north of Algeria using generalized regression neural networks (GRNN) and radial basis function neural networks (RBFNN …
I Ladlani, L Houichi, L Djemili, S Heddam, K Belouz
Meteorology and Atmospheric Physics 118 (3), 163-178, 2012
592012
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
522018
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
452017
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 (1), 153-165, 2016
422016
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
412020
Generalized regression neural network-based approach for modelling hourly dissolved oxygen concentration in the Upper Klamath River, Oregon, USA
S Heddam
Environmental technology 35 (13), 1650-1657, 2014
412014
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
412014
Modelling hourly dissolved oxygen concentration (DO) using dynamic evolving neural-fuzzy inference system (DENFIS)-based approach: case study of Klamath River at Miller Island …
S Heddam
Environmental Science and Pollution Research 21 (15), 9212-9227, 2014
402014
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
382011
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 (1), 402-420, 2019
352019
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
312019
An evaluation of ANN methods for estimating the lengths of hydraulic jumps in U-shaped channel
L Houichi, N Dechemi, S Heddam, B Achour
Journal of Hydroinformatics 15 (1), 147-154, 2013
312013
Secchi disk depth estimation from water quality parameters: artificial neural network versus multiple linear regression models?
S Heddam
Environmental processes 3 (2), 525-536, 2016
282016
Use of optimally pruned extreme learning machine (OP-ELM) in forecasting dissolved oxygen concentration (DO) several hours in advance: a case study from the Klamath River …
S Heddam
Environmental Processes 3 (4), 909-937, 2016
252016
Evaporation modelling by heuristic regression approaches using only temperature data
O Kisi, S Heddam
Hydrological Sciences Journal 64 (6), 653-672, 2019
242019
A new approach based on the dynamic evolving neural-fuzzy inference system (DENFIS) for modelling coagulant dosage (Dos): case study of water treatment plant of Algeria
S Heddam, N Dechemi
Desalination and water treatment 53 (4), 1045-1053, 2015
222015
Simultaneous modelling and forecasting of hourly dissolved oxygen concentration (DO) using radial basis function neural network (RBFNN) based approach: a case study from the …
S Heddam
Modeling Earth Systems and Environment 2 (3), 1-18, 2016
202016
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