Salim Heddam

发表

Comparison of Evolving Connectionist Systems (ECoS) and Neural Networks for Modelling Daily Pan Evaporation from Algerian Dam Reservoirs

Abderrazek Sebbar, Salim Heddam, Ozgur Kisi, 2020 .

Designing a New Data Intelligence Model for Global Solar Radiation Prediction: Application of Multivariate Modeling Scheme

Nadhir Al-Ansari, Hossein Bonakdari, Isa Ebtehaj, 2019, Energies.

Modeling daily dissolved oxygen concentration using modified response surface method and artificial neural network: a comparative study

Salim Heddam, Behrooz Keshtegar, B. Keshtegar, 2017, Neural Computing and Applications.

New Formulation for Predicting Daily Reference Evapotranspiration (ET0) in the Mediterranean Region of Algeria Country: Optimally Pruned Extreme Learning Machine (OPELM) Versus Online Sequential Extreme Learning Machine (OSELM)

Abderrazek Sebbar, Salim Heddam, Ozgur Kisi, 2020 .

Rainfall Pattern Forecasting Using Novel Hybrid Intelligent Model Based ANFIS-FFA

Zaher Mundher Yaseen, Hossein Bonakdari, Isa Ebtehaj, 2017, Water Resources Management.

An evaluation of ANN methods for estimating the lengths of hydraulic jumps in U-shaped channel

Bachir Achour, Salim Heddam, Larbi Houichi, 2013 .

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

Salim Heddam, Noureddine Dechemi, S. Heddam, 2015 .

Predicting Effluent Biochemical Oxygen Demand in a Wastewater Treatment Plant Using Generalized Regression Neural Network Based Approach: A Comparative Study

Salim Heddam, Hilal Lamda, Samir Filali, 2016, Environmental Processes.

Time series forecasting of river flow using an integrated approach of wavelet multi-resolution analysis and evolutionary data-driven models. A case study: Sebaou River (Algeria)

Sungwon Kim, Salim Heddam, Mousaab Zakhrouf, 2018 .

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, Oregon, USA

Salim Heddam, S. Heddam, 2016, Environmental Processes.

Fuzzy Neural Network (EFuNN) for Modelling Dissolved Oxygen Concentration (DO)

Salim Heddam, 2016, Intelligence Systems in Environmental Management.

Modeling hourly dissolved oxygen concentration (DO) using two different adaptive neuro-fuzzy inference systems (ANFIS): a comparative study

Salim Heddam, S. Heddam, 2013, Environmental Monitoring and Assessment.

Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors

Ozgur Kisi, Salim Heddam, O. Kisi, 2017, Environmental Science and Pollution Research.

Generalized Regression Neural Network Based Approach as a New Tool for Predicting Total Dissolved Gas (TDG) Downstream of Spillways of Dams: a Case Study of Columbia River Basin Dams, USA

Salim Heddam, S. Heddam, 2017, Environmental Processes.

Modelling Reference Evapotranspiration using a Novel Regression-based Method: Radial basis M5 Model Tree

Ozgur Kisi, Salim Heddam, Nguyen-Thoi Trung, 2021 .

Extremely randomized tree: a new machines learning method for predicting coagulant dosage in drinking water treatment plant

Salim Heddam, S. Heddam, 2021 .

Modelling daily soil temperature by hydro-meteorological data at different depths using a novel data-intelligence model: deep echo state network model

Bahram Gharabaghi, Salim Heddam, Meysam Alizamir, 2020, Artificial Intelligence Review.

A new heuristic model for monthly streamflow forecasting

Salim Heddam, Özgur Kişi, O. Kisi, 2021, Advances in Streamflow Forecasting.

Comparison of the advanced machine learning methods for better prediction accuracy of solar radiation using only temperature data: A case study

Ozgur Kisi, Salim Heddam, Mojtaba Mehraein, 2021, International Journal of Energy Research.

New formulation for predicting soil moisture content using only soil temperature as predictor: multivariate adaptive regression splines versus random forest, multilayer perceptron neural network, M5Tree, and multiple linear regression

Salim Heddam, S. Heddam, 2021 .

Modelling of Maximum Daily Water Temperature for Streams: Optimally Pruned Extreme Learning Machine (OPELM) versus Radial Basis Function Neural Networks (RBFNN)

Salim Heddam, Senlin Zhu, Senlin Zhu, 2019, Environmental Processes.

Rebuttal to “Estimation of dissolved oxygen using data-driven techniques in the Tai Po River, Hong Kong Samira Nemati, Mohammad Hasan Fazelifard, Ozlem Terzi and Mohammad Ali Ghorbani. Environ Earth Science (2015).” Doi:10.1007/s12665-015-4450-3

Salim Heddam, 2016, Environmental Earth Sciences.

Multilayer perceptron neural network-based approach for modeling phycocyanin pigment concentrations: case study from lower Charles River buoy, USA

Salim Heddam, S. Heddam, 2016, Environmental Science and Pollution Research.

Modelling hourly dissolved oxygen concentration (DO) using dynamic evolving neural-fuzzy inference system (DENFIS)-based approach: case study of Klamath River at Miller Island Boat Ramp, OR, USA

Salim Heddam, S. Heddam, 2014, Environmental Science and Pollution Research.

Modelling of daily lake surface water temperature from air temperature: Extremely randomized trees (ERT) versus Air2Water, MARS, M5Tree, RF and MLPNN

Mariusz Ptak, Salim Heddam, Senlin Zhu, 2020 .

Predicting Total Dissolved Gas Concentration on a Daily Scale Using Kriging Interpolation, Response Surface Method and Artificial Neural Network: Case Study of Columbia River Basin Dams, USA

Ozgur Kisi, Salim Heddam, Behrooz Keshtegar, 2019, Natural Resources Research.

Secchi Disk Depth Estimation from Water Quality Parameters: Artificial Neural Network versus Multiple Linear Regression Models?

Salim Heddam, S. Heddam, 2016, Environmental Processes.

A hybrid model for modelling the salinity of the Tafna River in Algeria

Salim Heddam, Boualem Remini, Abdelouhab Lefkir, 2019, Journal of Water and Land Development.

Extreme learning machine-based prediction of daily water temperature for rivers

Salim Heddam, Shiqiang Wu, Jiangyu Dai, 2019, Environmental Earth Sciences.

Modeling total dissolved gas (TDG) concentration at Columbia river basin dams: high-order response surface method (H-RSM) vs. M5Tree, LSSVM, and MARS

Ozgur Kisi, Salim Heddam, Shun-Peng Zhu, 2019, Arabian Journal of Geosciences.

Generalized regression neural network (GRNN)-based approach for colored dissolved organic matter (CDOM) retrieval: case study of Connecticut River at Middle Haddam Station, USA

Salim Heddam, S. Heddam, 2014, Environmental Monitoring and Assessment.

New modelling strategy based on radial basis function neural network (RBFNN) for predicting dissolved oxygen concentration using the components of the Gregorian calendar as inputs: case study of Clackamas River, Oregon, USA

Salim Heddam, S. Heddam, 2016, Modeling Earth Systems and Environment.

SVR-RSM: a hybrid heuristic method for modeling monthly pan evaporation

Abderrazek Sebbar, Salim Heddam, Shun-Peng Zhu, 2019, Environmental Science and Pollution Research.

Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models

Salim Heddam, Marijana Hadzima-Nyarko, Emmanuel Karlo Nyarko, 2018, Environmental Science and Pollution Research.

Estimation of Daily Reference Evapotranspiration (ET0) in the North of Algeria Using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR) Models: A Comparative Study

Salim Heddam, Lakhdar Djemili, Larbi Houichi, 2014, Arabian Journal for Science and Engineering.

Application of artificial intelligence to estimate phycocyanin pigment concentration using water quality data: a comparative study

Ozgur Kisi, Hadi Sanikhani, Salim Heddam, 2019, Applied Water Science.

Assessing the performance of a suite of machine learning models for daily river water temperature prediction

Salim Heddam, Marijana Hadzima-Nyarko, Emmanuel Karlo Nyarko, 2019, PeerJ.

Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and M5 model tree

Ozgur Kisi, Salim Heddam, O. Kisi, 2018 .

The implementation of univariable scheme-based air temperature for solar radiation prediction: New development of dynamic evolving neural-fuzzy inference system model

Zaher Mundher Yaseen, Ozgur Kisi, Salim Heddam, 2019, Applied Energy.

Prediction of dissolved oxygen in urban rivers at the Three Gorges Reservoir, China: extreme learning machines (ELM) versus artificial neural network (ANN)

Salim Heddam, Senlin Zhu, Senlin Zhu, 2020, Water Quality Research Journal.

Simultaneous modelling and forecasting of hourly dissolved oxygen concentration (DO) using radial basis function neural network (RBFNN) based approach: a case study from the Klamath River, Oregon, USA

Salim Heddam, S. Heddam, 2016, Modeling Earth Systems and Environment.

Evaporation modelling by heuristic regression approaches using only temperature data

Ozgur Kisi, Salim Heddam, O. Kisi, 2019, Hydrological Sciences Journal.

Generalized regression neural network-based approach for modelling hourly dissolved oxygen concentration in the Upper Klamath River, Oregon, USA

Salim Heddam, S. Heddam, 2014, Environmental technology.

Evolving connectionist systems (ECoSs): a new approach for modeling daily reference evapotranspiration (ET0)

Michael J. Watts, Abderrazek Sebbar, Salim Heddam, 2018, Environmental Monitoring and Assessment.

The employment of polynomial chaos expansion approach for modeling dissolved oxygen concentration in river

Salim Heddam, Behrooz Keshtegar, B. Keshtegar, 2019, Environmental Earth Sciences.