Mohammad Ali Ghorbani

Environmental Engineering · 2008–2018

A researcher in the Science corpus: 9 papers; trusts 12 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

Whom Mohammad Ali Ghorbani trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Ravinesh C. Deo
  2. Tianfeng Chai
  3. Hsuan-Tien Lin
  4. M. Günay
  5. Jan Adamowski
  6. Abdollah Kavousi-fard
  7. Zaher Mundher Yaseen
  8. Floris Takens
  9. Xin-She Yang
  10. Mesut Cimen

Who trusts Mohammad Ali Ghorbani overall

  1. Babak Mohammadi
  2. Ozgur Kisi
  3. Babak Jahani
  4. Roozbeh Moazenzadeh
  5. Pouya Aghelpour
  6. Ravinesh C. Deo
  7. Salim Heddam
  8. Zaher Mundher Yaseen

Papers from Mohammad Ali Ghorbani's perspective

Ordered by how strongly the researchers Mohammad Ali Ghorbani trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Support-Vector Networks
  2. Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand…
  3. Bootstrap rank‐ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling
  4. Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes
  5. Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques
  6. Artificial intelligence based models for stream-flow forecasting: 2000-2015
  7. Application of firefly algorithm-based support vector machines for prediction of field capacity and permanent wilting point
  8. Performance metrics for climate models
  9. Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey
  10. A new hybrid Modified Firefly Algorithm and Support Vector Regression model for accurate Short Term Load Forecasting (Expert systems with applications)

Their papers