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.
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.
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.
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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Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review
Vahid Nourani;Aida Hosseini Baghanam;Jan Adamowski;Ozgur Kisi.
Journal of Hydrology (2014)
A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling
Vahid Nourani;Mehdi Komasi;Akira Mano.
Water Resources Management (2009)
A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation
Vahid Nourani;Mohammad T. Alami;Mohammad H. Aminfar.
Engineering Applications of Artificial Intelligence (2009)
Two hybrid Artificial Intelligence approaches for modeling rainfall–runoff process
Vahid Nourani;Vahid Nourani;Özgür Kisi;Mehdi Komasi.
Journal of Hydrology (2011)
Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models.
Taher Rajaee;Seyed Ahmad Mirbagheri;Mohammad Zounemat-Kermani;Vahid Nourani.
Science of The Total Environment (2009)
An ANN‐based model for spatiotemporal groundwater level forecasting
Vahid Nourani;Asghar Asghari Mogaddam;Ata Ollah Nadiri.
Hydrological Processes (2008)
River Suspended Sediment Load Prediction: Application of ANN and Wavelet Conjunction Model
Taher Rajaee;Vahid Nourani;Mohammad Zounemat-Kermani;Ozgur Kisi.
Journal of Hydrologic Engineering (2011)
Sensitivity analysis of the artificial neural network outputs in simulation of the evaporation process at different climatologic regimes
Vahid Nourani;Mina Sayyah Fard.
Advances in Engineering Software (2012)
Using self-organizing maps and wavelet transforms for space–time pre-processing of satellite precipitation and runoff data in neural network based rainfall–runoff modeling
Vahid Nourani;Vahid Nourani;Aida Hosseini Baghanam;Aida Hosseini Baghanam;Jan Adamowski;Mekonnen Gebremichael.
Journal of Hydrology (2013)
Landslide susceptibility mapping at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural network models
Vahid Nourani;Biswajeet Pradhan;Hamid Ghaffari;Seyed Saber Sharifi.
Natural Hazards (2014)
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