A. H. Baghanam
Environmental Engineering · 2014
A researcher in the Science corpus: 1 paper; trusts 15 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Environmental Engineering — 31 citations into it; #3,721 of 5,479 by trust here, #1,854 by citations
- Water Science and Technology — 18 citations into it; #1,009 of 1,711 by trust here, #666 by citations
- Electrical and Electronic Engineering — 13 citations into it; #10,790 of 16,539 by trust here, #7,640 by citations
- Global and Planetary Change — 13 citations into it
- Artificial Intelligence — 11 citations into it
Whom A. H. Baghanam trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ozgur Kisi
- Vahid Nourani
- Stéphane Mallat
- Vladimir Naumovich Vapni
- Vladimir Naumovich Vapnik
- Simon S. Haykin
- David E. Goldberg
- John R. Koza
- Jalal Shiri
- Yanfang Sang
Who trusts A. H. Baghanam overall
Papers from A. H. Baghanam's perspective
Ordered by how strongly the researchers A. H. Baghanam trusts endorse each paper. Same corpus, a different order for every researcher.
- Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications (Environmental Modelling & Software)
- Data-driven modelling: some past experiences and new approaches
- Review of the self-organizing map (SOM) approach in water resources: Analysis, modelling and application (Environmental Modelling & Software)
- Neural Networks: A Comprehensive Foundation
- The Nature of Statistical Learning Theory
- A review on the applications of wavelet transform in hydrology time series analysis
- Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds.
- Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand…
- Detecting change in vague interpretations of landscapes
- Development of a new method of wavelet aided trend detection and estimation
Their papers
- Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review (2014) — 675 citations