FLOOD STAGE FORECASTING WITH SUPPORT VECTOR MACHINES 1

By Shie-Yui Liong, C. Sivapragasam. 2002

326 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/b5d8a6468c63188d88bcc3ff3ea0be415cf168eb

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In Environmental Engineering

In Global and Planetary Change

Citations in the corpus

Soil Water Simulation and Predication Using Stochastic Models Based on LS-SVM for Red Soil Region of China (2011, methodology): “In the past several years, various stochastic models have been applied in water resources and hydrology, including soil water simulation by Artificial Neural Networks and Support vector machine (Dibike et al. 2001; Baghdadi et al. 2002; Liong and Sivapragasam 2002; Kim and Valdes 2003; Liu et al. ”
Combing Random Forest and Least Square Support Vector Regression for Improving Extreme Rainfall Downscaling (2019, methodology): “For instance, SVM performed better than ANN in predicting groundwater levels [25], runoff and sediment yield simulation [26], flood stage prediction [27], rainfall–runoff modeling [28], river flow forecasting [29,30], long-term discharge prediction [31], and modeling discharge-suspended sediment rel”
Monthly Rainfall Forecasting Using EEMD-SVR Based on Phase-Space Reconstruction (2016, background): “…2006; Feng et al. 2015), typhoon rainfall (Lin et al. 2009), groundwater level (Suryanarayana et al. 2014), rainfall runoff (Wang et al. 2013), real-time daily flow (Maheswaran and Khosa 2013), lake water level (Khan and Coulibaly 2006), and riverine suspended sediment load (Nourani et al. 2016).”
Approximating SWAT Model Using Artificial Neural Network and Support Vector Machine 1 (2009, unknown): “Another learning machine – SVM, which has exhibited learning ability equal to or better than ANN for hydrologic simulation (Liong and Sivapragasam, 2002; Gill et al. , 2006b), was also taken as a promising candidate learning machine.”
Wavelet Genetic Algorithm-Support Vector Regression (Wavelet GA-SVR) for Monthly Flow Forecasting (2015, background): “…in hydrology such as: to predict soil moisture (Gill et al. 2006); to predict rainfall runoff (Dibike et al. 2001); to predict stream flow or stage (Liong and Sivapragasam 2002; Asefa et al. 2006; Yu et al. 2006); and to predict lake water level (Asefa et al. 2005; Khan and Coulibaly 2006).”
Data-driven modelling: some past experiences and new approaches (2008, methodology): “(2001) and Liong & Sivapragasam (2002) reported using SVMs forfloodmanagementand inpredictionof riverwaterflowsand stages.”