Using support vector machines for long-term discharge prediction

By Jianyi Lin, Chun-tian Cheng, Kwok-Wing Chau. 2006

568 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/45f0e02b2508999368aeaff25ebb7056807509b7

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In Environmental Engineering

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series (2009, methodology): “The SVM method has also been employed for stream flow predictions (Asefa et al., 2006; Lin et al., 2006).”
Wavelet Analysis-Support Vector Machine Coupled Models for Monthly Rainfall Forecasting in Arid Regions (2015, methodology): “Statistical procedures were suggested for identifying the appropriate input vector for a model (Sudheer et al. 2002; Lin et al. 2006).”
Comparative study of monthly inflow prediction methods for the Three Gorges Reservoir (2014, methodology): “2005), SCE-UA algorithm (Lin et al. 2006).”
Flood Prediction Using Machine Learning Models: Literature Review (2018, methodology): “The IIS–W-ANN hybrid model outperformed the M5 tree.”
Combing Random Forest and Least Square Support Vector Regression for Improving Extreme Rainfall Downscaling (2019, methodology): “SVM is also superior to multiple linear regression (MLR) in streamflow forecasting [33], autoregressive moving average (ARMA) in discharge prediction [31,34], autoregressive integrated moving average (ARIMA) in streamflow prediction [35], neural networks (NN), and MLR in daily water demand and inflo”
Reservoir flood control operation based on chaotic particle swarm optimization algorithm (2014, methodology): “With the development of the fusion of water conservancy engineering and computing science by artificial intelligence technology and intelligent optimization method [3–7], many scholars have begun to introduce various optimal algorithms into develop the study on the flood control operation [8–10].”
A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (2017, methodology): “SVM performed comparatively well in predictions of reservoir inflow (Lin et al., 2006), soil moisture retrieval using vegetation indices and precipitation (Ahmad et al.”
Assessment of evolutionary algorithms in predicting non-deposition sediment transport (2016, methodology): “…complicated problems, these methods have been extensively applied, during the past decades, in water resources and hydraulic structures (Baghalian et al., 2012; Bonakdari et al., 2011; Kisi, 2008; Lin et al., 2006; Muzzammil, 2008; Tayfur & Guldal, 2006; Wang et al., 2009; Wu et al., 2010).”
Evaluating a coupled discrete wavelet transform and support vector regression for daily and monthly streamflow forecasting (2014, methodology): “The SVM model can thus achieve an optimum network structure and better generalization using the SRM principle (Lin et al., 2006).”