Application of support vector machine and relevance vector machine to determine evaporative losses in reservoirs

By Pijush Samui, Barnali Dixon. 2012

84 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/b7e1a18fcc672a924e19124e07e5d2800dcef645

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. Ravinesh C. Deo
2. Dookie Kim
3. Pijush Samui
4. Ozgur Kisi
5. Salim Heddam

In Electrical and Electronic Engineering

1. Ravinesh C. Deo
2. Dookie Kim
3. Pijush Samui

In Environmental Engineering

1. Ravinesh C. Deo
2. Dookie Kim
3. Pijush Samui
4. Ozgur Kisi
5. Salim Heddam
6. Zaher Mundher Yaseen
7. Anurag Malik
8. Anil Kumar

Citations in the corpus

• Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models (2016, methodology): “Following an earlier works [e.g. (Samui and Dixon 2012)], out of 528 months of predictor dataset, a total of 370 monthly values (&70”