A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer

By Heesung Yoon, Seong-Chun Jun, Yunjung Hyun, G. Bae, Kang-Kun Lee. 2011

588 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/c69a1533b68f9e11dc6ac8c83f9ff2ff04d96709

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In Environmental Engineering

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Comparative study of monthly inflow prediction methods for the Three Gorges Reservoir (2014, methodology): “As it has good generalization ability with a finite data sample, 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 ”
Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data (2017, background): “…need groundwater-level fluctuations time series data (normally over a long period) and probably some other related input variables (such as precipitation); however, the outcomes of these models are confined to predicting temporal changes at a specific area (Yoon et al. 2011; Khalil et al. 2015).”
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”
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.”
Long-term monthly average temperature forecasting in some climate types of Iran, using the models SARIMA, SVR, and SVR-FA (2019, background): “For detailed information, refer to Jha and Hayashi (2014), Pai and Hong (2007), Moazenzadeh et al. (2018), Isazadeh et al. (2017), and Yoon et al. (2011).”
Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review (2014, background): “…et al. (2009) Groundwater modeling Bhattacharjya and Datta (2009) He et al. (2008) Fallah-Mehdipour et al. (2013) Bhattacharjya and Datta (2009) Yoon et al. (2011) Water quality modeling Singh et al. (2009) Pai et al. (2009) Eslamian and Lavaei (2009) Dhar and Datta (2009) Singh et al.…”