River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches

By Bellie Sivakumar, A. Jayawardena, T. Fernando. 2002

332 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/3eadf226d3561378ba5885fe091de062ecac77e4

How researchers see this paper

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

In Environmental Engineering

1. Ozgur Kisi
2. Wai Keung Li
3. Kwok-Wing Chau
4. C. Wu
5. A. Jayawardena
6. Bellie Sivakumar
7. Yan Li
8. Vahid Nourani

In Global and Planetary Change

1. Wai Keung Li
2. A. Jayawardena
3. Bellie Sivakumar
4. C. Wu
5. Kwok-Wing Chau
6. Yan Li
7. Ozgur Kisi
8. Hossein Bonakdari

In Water Science and Technology

1. C. Wu
2. Kwok-Wing Chau
3. Wai Keung Li
4. Yan Li
5. Bellie Sivakumar
6. A. Jayawardena
7. Ozgur Kisi
8. Jan Adamowski

Citations in the corpus

• Monthly Rainfall Forecasting Using EEMD-SVR Based on Phase-Space Reconstruction (2016, result): “According to previous studies (e.g. Sivapragasam et al. 2001; Liong and Sivapragasam 2002), SVM can better solve problems of small sample size, overlearning, nonlinearity, high dimensionality, and local minima than ANN can, and has high generalization capability (Wang et al. 2013).”
• Singular spectrum analysis and forecasting of hydrological time series (2006, methodology): “, have also been used in forecasting hydrologic variables (e.g. French et al., 1992; Hsu et al., 1995; Zealand et al., 1999; Sivakumar et al., 2002; Nayak et al., 2004).”
• The Research of Monthly Discharge Predictor-corrector Model Based on Wavelet Decomposition (2008, background): “ Salas et al. (1980) gave a detailed introduction concerning the procedures necessary in constructing a suitable model.”
• Predicting monthly streamflow using data‐driven models coupled with data‐preprocessing techniques (2009, background): “…have gained the increasing interest of the hydrology community in the past two decades [Jayawardena and Lai, 1994; Jayawardena and Gurung, 2000; Elshorbagy et al., 2002; Sivakumar et al., 2002] in which a streamflow series was assumed to be derived from a nonlinear deterministic dynamic system.”
• Hydrologic complexity and classification: a simple data reconstruction approach (2007, methodology): “…purpose of brevity, details of correlation dimension and system identification are not reported herein, but can be found in a number of studies (e.g. Grassberger and Procaccia, 1983a,b; Fraedrich, 1986; Rodriguez-Iturbe et al., 1989; Tsonis et al., 1993; Sivakumar et al., 2002b; Sivakumar, 2005b).”