Support vector regression for real-time flood stage forecasting

By Pao-Shan Yu, Shien-Tsung Chen, I-F. Chang. 2006

560 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/fa4e73d8fef3bd1f40a3cf101ef97dcff25a8eba

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In Environmental Engineering

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting (2014, background): “Despite the promising SVR demonstrated in [21-24], the applications of SVR in interval-valued time series (ITS) have not been widely explored.”
Modelling long‐term monthly temperatures by several data‐driven methods using geographical inputs (2015, methodology): “Yu et al. (2006) applied SVR for real-time flood stage predicting.”
Review of surrogate modeling in water resources (2012, background): “SVMs can better handle larger numbers of design sites as the operator associated with design site vectors in the SVM formulation is dot product [Yu et al., 2006].”
A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (2017, methodology): “…using vegetation indices and precipitation (Ahmad et al., 2010), streamflow (Asefa et al., 2006), evapotranspiration (Tabari et al., 2012), lake level (Khan & Coulibaly, 2006), groundwater level (Yoon et al., 2011), flood stage (Yu et al., 2006), and sediment yield (Çimen, 2008), among others.”
Regional flood frequency analysis using support vector regression in arid and semi-arid regions of Iran (2018, methodology): “The support vector machine (SVM) is one of the most widely used groups of data-driven techniques in hydrology (Dibike et al. 2001, Liong and Sivapragasam 2002, Bray and Han 2004, Asefa et al. 2006, Lin et al. 2006, Yu et al. 2006, Afiq et al. 2013).”
Survey of computational intelligence as basis to big flood management: challenges, research directions and future work (2018, methodology): “…2012) PR Introducing a model to be used in wireless sensor network (WSN) for forecasting ﬂoods in rivers to provide reliable and timely warnings (Yu, Chen, & Chang, 2006) SVR Real-time ﬂood stage forecasting using SVR (Dai et al., 2011) Two-stage SVR Enhancing the analysis accuracy in…”
Comparative study of monthly inflow prediction methods for the Three Gorges Reservoir (2014, methodology): “…and performs better than the traditional models such as neural network model (Yoon et al. 2011), much research has been devoted to the formulation and development of SVM for improving the quality of hydrologic prediction (Bray and Han 2004; Yu et al. 2006; Lin et al. 2009; Noori et al. 2011).”
Predicting monthly streamflow using data‐driven models coupled with data‐preprocessing techniques (2009, background): “Similar to linear SVR [Kecman, 2001; Yu et al., 2006], the nonlinear SVR problem can be expressed as the following optimization problem: minimize R W ;xi;xi* ¼ 1 2 wk k2þ C XN i¼1 ðxi þ xi*Þ subject to yi f f Yið Þ;wð Þ b eþ xi f fðYiÞ;wð Þ þ b yi eþ xi* xi; xi* 0 8>< >: ; ð15Þ where Yi represents…”
Application of PCA, SVR, and ANFIS for modeling of rock fragmentation (2015, methodology): “Due to the advantages of the generalization capability in obtaining a unique solution, the SVMs have drawn the attention of researchers and have been applied in many applications (Liu et al. 2004; Yu et al. 2006; Zhao 2010; Khandelwal 2010; Shi et al. 2012; Mohamadnejad et al. 2012; Khandelwal and K”
M5 model tree application in daily river flow forecasting in Sohu Stream, Turkey (2013, methodology): “Keeping in view the better performance by radial basis function kernel function for various civil engineering applications [13, 17, 22, 23, 38], present study uses a”