What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
His primary areas of investigation include Artificial neural network, Wavelet, Artificial intelligence, Wavelet transform and Hydrology.
His Artificial neural network research incorporates themes from Range, Meteorology, Data mining and Linear regression.
His work deals with themes such as Moving average and Autoregressive model, which intersect with Wavelet.
Vahid Nourani combines subjects such as Machine learning and Pattern recognition with his study of Artificial intelligence.
His Wavelet transform research includes elements of Surface runoff and Seasonality.
His research integrates issues of Watershed and Adaptive neuro fuzzy inference system in his study of Surface runoff.
His most cited work include:
- Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review (328 citations)
- A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling (237 citations)
- Two hybrid Artificial Intelligence approaches for modeling rainfall–runoff process (213 citations)
What are the main themes of his work throughout his whole career to date?
Artificial neural network, Artificial intelligence, Wavelet, Hydrology and Wavelet transform are his primary areas of study.
His Artificial neural network research is multidisciplinary, incorporating perspectives in Data mining, Statistics, Linear regression, Support vector machine and Adaptive neuro fuzzy inference system.
The Artificial intelligence study which covers Machine learning that intersects with Process and Rainfall runoff.
His Wavelet study incorporates themes from Extreme learning machine, Streamflow and Autoregressive model.
His Wavelet transform study frequently draws parallels with other fields, such as Series.
Vahid Nourani combines subjects such as Watershed, Meteorology and Algorithm with his study of Surface runoff.
He most often published in these fields:
- Artificial neural network (64.96%)
- Artificial intelligence (40.55%)
- Wavelet (37.40%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial neural network (64.96%)
- Artificial intelligence (40.55%)
- Support vector machine (29.53%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial neural network, Artificial intelligence, Support vector machine, Adaptive neuro fuzzy inference system and Data mining.
His work deals with themes such as Ensemble learning, Prediction interval, Statistics and Wavelet, which intersect with Artificial neural network.
He works in the field of Wavelet, namely Wavelet transform.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning, Spatial distribution, Spatial estimation and Stage.
In Support vector machine, Vahid Nourani works on issues like Uncertainty analysis, which are connected to Outflow, Dam failure, Process, Monte Carlo method and Dependability.
He focuses mostly in the field of Adaptive neuro fuzzy inference system, narrowing it down to matters related to Ensemble forecasting and, in some cases, Black box and Feedforward neural network.
Between 2019 and 2021, his most popular works were:
- Artificial intelligence based ensemble model for prediction of vehicular traffic noise. (16 citations)
- Artificial intelligence based ensemble model for prediction of vehicular traffic noise. (16 citations)
- Multi-step ahead modeling of reference evapotranspiration using a multi-model approach (12 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Machine learning
- Artificial intelligence
Vahid Nourani focuses on Artificial neural network, Data mining, Precipitation, Artificial intelligence and Sensitivity.
His Artificial neural network research is multidisciplinary, incorporating elements of Ensemble forecasting, Black box, Adaptive neuro fuzzy inference system and Support vector machine.
His Support vector machine study combines topics in areas such as Ensemble learning, Neural ensemble and Linear regression.
In general Data mining study, his work on Decision tree often relates to the realm of Multivariable calculus, Test data and Rate of penetration, thereby connecting several areas of interest.
The Precipitation study combines topics in areas such as Spatial estimation, Spatial distribution, Temporal modeling and Stage.
In the subject of general Artificial intelligence, his work in Feedforward neural network is often linked to Noise pollution, thereby combining diverse domains of study.
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