Singular spectrum analysis and forecasting of hydrological time series

By C. Marques, Juan Ferreira, A. Rocha, J. Castanheira, P. Melo‐Gonçalves, N. Vaz, J. Dias. 2006

118 citations recorded by the source corpus.

Subfields: Applied Mathematics, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/096f2dea30baeb7d760d96f819d7d8ece9d728b7

How researchers see this paper

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

In Applied Mathematics

1. Petros Koumoutsakos

In Environmental Engineering

1. Kwok-Wing Chau
2. C. Wu
3. C. Fan
4. Shenglian Guo
5. Yanlai Zhou
6. Hua Chen
7. Yun Wang
8. Petros Koumoutsakos

In Global and Planetary Change

1. C. Wu
2. Shenglian Guo
3. Yanlai Zhou
4. Hua Chen
5. Yun Wang
6. Kwok-Wing Chau
7. Yan Li
8. C. Fan

Citations in the corpus

• Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks (2018, methodology): “…methods [7, 2, 3, 5, 6, 4, 8], model identification procedures [11, 10, 12, 13, 14, 15, 16, 9] and forecasting techniques [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] that aim to provide precise short-term predictions while capturing the long-term statistics of these systems.”
• Comparative study of monthly inflow prediction methods for the Three Gorges Reservoir (2014, methodology): “Current applications of SSA in hydrology only selected the filtered series as model inputs without considering noise series (Sivapragasam et al. 2001; Marques et al. 2006; Wu et al. 2009).”
• Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques (2010, background): “N., and Dias, 861  J.M. (2006), Singular spectral analysis and forecasting of hydrological time series.”
• Singular Spectrum Analysis and ARIMA Hybrid Model for Annual Runoff Forecasting (2011, methodology): “Lisi et al. (1995) built an adaptive noise-reduction algorithm based on Singular Spectrum Analysis and a standard feedforward neural prediction model.”
• Methods to improve neural network performance in daily flows prediction (2009, methodology): “69 The techniques of SSA and WMRA have been successfully introduced to the field of 70 hydrology (Lisi et al., 1995; Sivapragasam et al., 2001; Marques et al., 2006; Partal and 71 Kişi, 2007).”