What is she best known for?
The fields of study she is best known for:
- Statistics
- Machine learning
- Artificial neural network
Her primary scientific interests are in Statistics, Mean squared error, Froude number, Artificial neural network and Sensitivity.
Isa Ebtehaj interconnects Particle swarm optimization and Differential evolution in the investigation of issues within Statistics.
Her Mean squared error study incorporates themes from Hydrology, Coefficient of determination and Correlation coefficient.
Her Froude number research is multidisciplinary, incorporating elements of Sanitary sewer, Geotechnical engineering, Sediment transport and Dimensionless quantity.
The various areas that Isa Ebtehaj examines in her Dimensionless quantity study include Bed load, Sedimentation and Flow, Discharge coefficient.
Her Artificial neural network research includes themes of Genetic algorithm and Mathematical optimization.
Her most cited work include:
- Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model (92 citations)
- EVALUATION OF SEDIMENT TRANSPORT IN SEWER USING ARTIFICIAL NEURAL NETWORK (71 citations)
- Gene expression programming to predict the discharge coefficient in rectangular side weirs (71 citations)
What are the main themes of her work throughout her whole career to date?
Mean squared error, Froude number, Sediment transport, Artificial neural network and Algorithm are her primary areas of study.
Mean squared error is the subject of her research, which falls under Statistics.
Her work carried out in the field of Froude number brings together such families of science as Discharge coefficient, Mathematical analysis, Dimensionless quantity and Sensitivity.
In her work, Sedimentation and Backpropagation is strongly intertwined with Geotechnical engineering, which is a subfield of Sediment transport.
The Artificial neural network study combines topics in areas such as Data mining, Support vector machine and Regression.
Her work on Particle swarm optimization and Differential evolution as part of general Algorithm research is often related to Neuro-fuzzy, thus linking different fields of science.
She most often published in these fields:
- Mean squared error (32.46%)
- Froude number (31.58%)
- Sediment transport (21.93%)
What were the highlights of her more recent work (between 2019-2021)?
- Stochastic modelling (7.89%)
- Froude number (31.58%)
- Mean squared error (32.46%)
In recent papers she was focusing on the following fields of study:
Her main research concerns Stochastic modelling, Froude number, Mean squared error, Gene expression programming and Series.
Her studies in Stochastic modelling integrate themes in fields like Genetic algorithm, Normalization, Linear model and Climatology.
Her Froude number research is multidisciplinary, incorporating perspectives in Sediment transport, Mathematical analysis, Correlation coefficient, Mean absolute percentage error and Communication channel.
Her study looks at the relationship between Sediment transport and fields such as Soil science, as well as how they intersect with chemical problems.
Her Mean squared error study necessitates a more in-depth grasp of Statistics.
Her research integrates issues of Dimensionless quantity and Discharge coefficient in her study of Gene expression programming.
Between 2019 and 2021, her most popular works were:
- Evaluation of preprocessing techniques for improving the accuracy of stochastic rainfall forecast models (12 citations)
- Integrative stochastic model standardization with genetic algorithm for rainfall pattern forecasting in tropical and semi-arid environments (11 citations)
- Combination of sensitivity and uncertainty analyses for sediment transport modeling in sewer pipes (8 citations)
In her most recent research, the most cited papers focused on:
- Statistics
- Machine learning
- Chemistry
Isa Ebtehaj mainly focuses on Stochastic modelling, Uncertainty analysis, Communication channel, Mean squared error and Artificial neural network.
Her Stochastic modelling study combines topics in areas such as Genetic algorithm and Normalization.
Her study in Uncertainty analysis is interdisciplinary in nature, drawing from both Extreme learning machine, Sanitary sewer, Normal distribution, Algorithm and Vector field.
Her Communication channel research incorporates elements of Soil science, Froude number, Sediment transport and Sensitivity.
Her study with Mean squared error involves better knowledge in Statistics.
Preprocessor is closely connected to Series in her research, which is encompassed under the umbrella topic of Artificial neural network.
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