Discharge Rating Curve Extension – A New Approach

By C. Sivapragasam, Nitin Muttil. 2005

98 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/af7b19b73fc5d27dfd059644d6ec171a552839f7

How researchers see this paper

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

In Ecology

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

In Environmental Engineering

1. Ravinesh C. Deo
2. Dookie Kim
3. Pijush Samui
4. M. Ramirez
5. Dawei Han
6. Prashant Kumar Srivastava
7. Tanvir Islam
8. Ozgur Kisi

In Water Science and Technology

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

Citations in the corpus

• Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application (2013, background): “These artificial intelligence techniques have many merits over traditional modelling techniques due to their better ability to handle enormous amounts of noisy data from dynamic and non-linear systems (Remesan et al. 2009; Sivapragasam and Muttil 2005).”
• Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models (2016, methodology): “Several researchers use trial and error approaches for determination of the best performance of the RVM models (Khan and Coulibaly 2006; Pal 2006; Sivapragasam and Muttil 2005).”