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.
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