M
Mohammad Zounemat-Kermani
Researcher at Shahid Bahonar University of Kerman
Publications - 121
Citations - 3905
Mohammad Zounemat-Kermani is an academic researcher from Shahid Bahonar University of Kerman. The author has contributed to research in topics: Adaptive neuro fuzzy inference system & Artificial neural network. The author has an hindex of 27, co-authored 102 publications receiving 2427 citations. Previous affiliations of Mohammad Zounemat-Kermani include K.N.Toosi University of Technology & École Polytechnique Fédérale de Lausanne.
Papers
More filters
Journal ArticleDOI
Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models.
TL;DR: Results demonstrate that NF model presents better performance in SSC prediction in compression to other models; while ANN and NF models depict better results than MLR and SRC methods.
Journal ArticleDOI
Solar radiation prediction using different techniques: model evaluation and comparison
Lunche Wang,Ozgur Kisi,Mohammad Zounemat-Kermani,Germán Ariel Salazar,Zhongmin Zhu,Zhongmin Zhu,Wei Gong +6 more
TL;DR: In this paper, three types of Artificial Neural Network (ANN) methods, Multilayer Perceptron (MLP), Generalized Regression Neural Networks (GRNN) and Radial Basis Neural Network(RBNN) are applied for predicting the daily global solar radiation (Hg) using above meteorological variables as model inputs.
Journal ArticleDOI
Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs
Rana Muhammad Adnan,Zhongmin Liang,Salim Heddam,Mohammad Zounemat-Kermani,Ozgur Kisi,Binquan Li +5 more
TL;DR: In this paper, the prediction accuracy of new heuristic methods, optimally pruned extreme learning machine (OP-ELM), least square support vector machine (LSSVM), multivariate adaptive regression splines (MARS) and M5 model tree (M5Tree), is examined in modeling monthly streamflows using precipitation and temperature inputs.
Journal ArticleDOI
Using adaptive neuro-fuzzy inference system for hydrological time series prediction
TL;DR: A time series neuro-fuzzy model is proposed that is capable of exploiting the strengths of traditional time series approaches, considering different numbers of membership functions and compared to an autoregressive model.
Journal ArticleDOI
Ensemble machine learning paradigms in hydrology: A review
TL;DR: In this article, a review of ensemble learning methodologies in various areas of hydrology for simulation and prediction purposes has been presented, and the general findings demonstrate the absolute superiority of using ensemble strategies over the regular (individual) model learning in hydrology.