H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Engineering and Technology D-index 76 Citations 17,881 334 World Ranking 173 National Ranking 1

Overview

What is he best known for?

The fields of study he is best known for:

  • Statistics
  • Artificial neural network
  • Hydrology

His primary areas of investigation include Artificial neural network, Meteorology, Mean squared error, Statistics and Hydrology. The concepts of his Artificial neural network study are interwoven with issues in Sediment, Streamflow, Support vector machine and Regression. His research in Meteorology is mostly focused on Wind speed.

His studies examine the connections between Wind speed and genetics, as well as such issues in Evapotranspiration, with regards to Empirical modelling. His work deals with themes such as Gradient descent, Linear regression and Correlation coefficient, which intersect with Mean squared error. Ozgur Kisi interconnects Rating curve, Black box, Data mining and Conjugate gradient method in the investigation of issues within Hydrology.

His most cited work include:

  • Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review (328 citations)
  • Streamflow Forecasting Using Different Artificial Neural Network Algorithms (280 citations)
  • Suspended sediment estimation using neuro-fuzzy and neural network approaches (243 citations)

What are the main themes of his work throughout his whole career to date?

Ozgur Kisi focuses on Artificial neural network, Mean squared error, Statistics, Meteorology and Hydrology. The various areas that he examines in his Artificial neural network study include Sediment, Streamflow, Evapotranspiration and Support vector machine. The study incorporates disciplines such as Gene expression programming, Correlation coefficient and Coefficient of determination in addition to Mean squared error.

His Statistics study combines topics from a wide range of disciplines, such as Sunshine duration, Water quality and Tree. In the field of Meteorology, his study on Wind speed overlaps with subjects such as Air temperature. His Wind speed study combines topics in areas such as Relative humidity and Pan evaporation.

He most often published in these fields:

  • Artificial neural network (45.32%)
  • Mean squared error (38.46%)
  • Statistics (29.31%)

What were the highlights of his more recent work (between 2019-2021)?

  • Mean squared error (38.46%)
  • Statistics (29.31%)
  • Artificial neural network (45.32%)

In recent papers he was focusing on the following fields of study:

His scientific interests lie mostly in Mean squared error, Statistics, Artificial neural network, Artificial intelligence and Support vector machine. The Mean squared error study combines topics in areas such as Extreme learning machine, Streamflow, Multivariate statistics and Regression. His study in the field of Correlation coefficient also crosses realms of Root mean square.

His Artificial neural network research is multidisciplinary, incorporating perspectives in Decision tree, Group method of data handling and Particle swarm optimization. His research investigates the link between Artificial intelligence and topics such as Machine learning that cross with problems in Wavelet transform. His research integrates issues of Algorithm, Sediment and Scatter plot in his study of Support vector machine.

Between 2019 and 2021, his most popular works were:

  • Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs (46 citations)
  • Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm (42 citations)
  • Evaluation of mechanical properties of concretes containing coarse recycled concrete aggregates using multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), and least squares support vector regression (LSSVR) models (30 citations)

In his most recent research, the most cited papers focused on:

  • Statistics
  • Machine learning
  • Artificial intelligence

Ozgur Kisi mainly focuses on Mean squared error, Support vector machine, Artificial neural network, Artificial intelligence and Statistics. Ozgur Kisi combines subjects such as Soil science, Drainage basin, Streamflow, Regression and Extreme learning machine with his study of Mean squared error. His studies in Support vector machine integrate themes in fields like Correlation coefficient, Radial basis function, Multivariate adaptive regression splines, Autoregressive model and Hydrogeology.

His work carried out in the field of Artificial neural network brings together such families of science as Wind speed and Particle swarm optimization. His Wind speed research includes themes of Pearson product-moment correlation coefficient and Evapotranspiration. His work is dedicated to discovering how Statistics, Genetic algorithm are connected with k-means clustering, Groundwater and Fuzzy logic and other disciplines.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review

Vahid Nourani;Aida Hosseini Baghanam;Jan Adamowski;Ozgur Kisi.
Journal of Hydrology (2014)

440 Citations

Suspended sediment estimation using neuro-fuzzy and neural network approaches/Estimation des matières en suspension par des approches neurofloues et à base de réseau de neurones

Ozgur Kisi.
Hydrological Sciences Journal-journal Des Sciences Hydrologiques (2005)

372 Citations

Suspended sediment estimation using neuro-fuzzy and neural network approaches

Ozgur Kisi.
Hydrological Sciences Journal-journal Des Sciences Hydrologiques (2005)

366 Citations

Streamflow Forecasting Using Different Artificial Neural Network Algorithms

Özgür Kişi.
Journal of Hydrologic Engineering (2007)

362 Citations

River Flow Modeling Using Artificial Neural Networks

Özgür Kişi.
Journal of Hydrologic Engineering (2004)

354 Citations

Wavelet and neuro-fuzzy conjunction model for precipitation forecasting

Turgay Partal;Özgür Kişi.
Journal of Hydrology (2007)

311 Citations

Two hybrid Artificial Intelligence approaches for modeling rainfall–runoff process

Vahid Nourani;Vahid Nourani;Özgür Kisi;Mehdi Komasi.
Journal of Hydrology (2011)

310 Citations

Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation / Prévision et estimation de la concentration en matières en suspension avec des perceptrons multi-couches et l’algorithme d’apprentissage de Levenberg-Marquardt

Özgür Kisi.
Hydrological Sciences Journal-journal Des Sciences Hydrologiques (2004)

298 Citations

A wavelet-support vector machine conjunction model for monthly streamflow forecasting

Ozgur Kisi;Mesut Cimen.
Journal of Hydrology (2011)

275 Citations

Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data

H. Kerem Cigizoglu;Özgür Kişi.
Hydrology Research (2005)

239 Citations

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