An ensemble of neural networks for weather forecasting

By Imran Maqsood, Muhammad Riaz Khan, Ajith Abraham. Neural computing & applications (Print), 2004

363 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/79beca7af27c6847db2ad224da92c5b138efc085

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yanfei Kang
2. Rob J Hyndman
3. Feng Li
4. Xiaoqian Wang
5. Weizhong Yan
6. Christoph Bergmeir
7. Kasun Bandara
8. Slawek Smyl

In Electrical and Electronic Engineering

1. Rob J Hyndman
2. Weizhong Yan
3. Tao Hong
4. Han Lin Shang
5. Rafal Weron

In Environmental Engineering

1. Weizhong Yan

Citations in the corpus

• Toward Automatic Time-Series Forecasting Using Neural Networks (2012, methodology): “[16] used an ensemble of different types of networks for weather forecasting in southern Saskatchewan, Canada.”
• Forecast combinations: An over 50-year review (2022, background): “…weather forecast is constructed from a set of point forecasts of the same weather quantity of interest, based on perturbed initial atmospheric states (e.g., Maqsood et al., 2004; Gneiting and Raftery, 2005) and/or different model formulations (e.g., Buizza et al., 1999; Buizza et al., 2005).”