Extreme Learning Machines: A new approach for prediction of reference evapotranspiration

By S. Abdullah, M. Malek, Namiq Abdullah, Ozgur Kisi, Keem Siah Yap. 2015

230 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/4a1da8d29a720260c62efe1f99eb6fe3f9284f46

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. Jan Adamowski
2. John M. Quilty
3. Mukesh Tiwari
4. Dimitrios Kateris
5. Remigio Berruto
6. Georgios Dolias
7. Dionysis D. Bochtis
8. Ravinesh C. Deo

In Environmental Engineering

1. Ravinesh C. Deo
2. Mukesh Tiwari
3. John M. Quilty
4. Jan Adamowski
5. Anurag Malik
6. Anil Kumar
7. Zaher Mundher Yaseen
8. Ozgur Kisi

In Renewable Energy, Sustainability and the Environment

1. Ravinesh C. Deo

Citations in the corpus

• Machine Learning in Agriculture: A Comprehensive Updated Review (2021, background): “In many cases, accurate estimation of ET (the summation of the transpiration via the plant canopy and the evaporation from plant, soil, and open water surface) is among the most central elements of hydrologic cycle for optimal management of water resources [97].”
• Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model (2017, background): “Abdullah et al. (2015) showed that ELM is efficient, simple in application, fast, and possesses good generalization performance for simulating Penman–Monteith evapotranspiration in Iraq.”