Model Induction with Support Vector Machines: Introduction and Applications

By Yonas B. Dibike, Slavco Velickov, Dimitri P. Solomatine, M. Abbott. 2001

592 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Civil and Structural Engineering, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/d08b933458716a85888956165d07243505d4e4b2

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Citations in the corpus

Development of Pedotransfer Functions for Estimation of Soil Hydraulic Parameters using Support Vector Machines (2009, methodology): “Dibike et al. (2001) applied SVM to remotely sensed image-processing problems and reported a superior performance over the traditional ANNs.”
Design and evaluation of SVR, MARS and M5Tree models for 1, 2 and 3-day lead time forecasting of river flow data in a semiarid mountainous catchment (2018, methodology): “A number of different SVR methods have been developed to forecast the river flow time series during the last two decades (Asefa et al. 2006; Behzad et al. 2009; Ch et al. 2013; Dibike et al. 2001; Li et al. 2010).”
EC-SVM approach for real-time hydrologic forecasting (2004, background): “Studies such as Dibike et al. (2001) and Liong & Sivapragasm (2002) show the suitability of the Gaussian kernel function in hydrological time series.”
Wavelet Genetic Algorithm-Support Vector Regression (Wavelet GA-SVR) for Monthly Flow Forecasting (2015, background): “…and regression technique has gained the attention 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…”
Flood Prediction Using Machine Learning Models: Literature Review (2018, background): “and nonlinear classification, and the efficient mapping of inputs into feature spaces [119].”
Data-driven modelling: some past experiences and new approaches (2008, background): “Traditionally the river basin (watershed) was treated as a lumped, time-invariant, linear, deterministic system, resulting in the unit hydrograph theory (Sherman 1932), upon which the Nash linear cascade of reservoirs model (Nash 1957) and the parallel cascade of reservoirs of Diskin (1964) were bui”
A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series (2009, methodology): “Dibike et al. (2001) demonstrated its use in rainfall runoff modeling.”
Long-term monthly average temperature forecasting in some climate types of Iran, using the models SARIMA, SVR, and SVR-FA (2019, methodology): “Dibike et al. (2001) firstly applied the support vector machines (SVM) approach for accurate simulation of rainfall-runoff processes in hydrology.”
A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer (2011, background): “Many researchers have verified the SVM performance for the prediction of rainfall runoff (Dibike et al., 2001), stream flow or stage (Liong and Sivapragasam, 2002; Asefa et al.”
A Comparison between Regression, Artificial Neural Networks and Support Vector Machines for Predicting Stock Market Index (2015, background): “the ability to map non-linearly the training data and the ease of implementation [37]–[39].”