Nonparametric Regression via<b>StatLSSVM</b>

Brabanter, Kris De; Suykens, Johan A. K.; Moor, Bart De

doi:10.18637/jss.v055.i02

Cited by 24 publications

(16 citation statements)

References 74 publications

(41 reference statements)

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“…The tradeoff problem can be resolved by performing a fine tuning by the DSS. Thus, the coupled CSA and DSS result in better performance and more optimal solutions [97,98].…”

Section: Models Ds and As Input Variables Output Variablesmentioning

confidence: 99%

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

2018

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Accurate modeling for nonlinear and nonstationary rainfall-runoff processes is essential for performing hydrologic practices effectively. This paper proposes two hybrid machine learning models (MLMs) coupled with variational mode decomposition (VMD) to enhance the accuracy for daily rainfall-runoff modeling. These hybrid MLMs consist of VMD-based extreme learning machine (VMD-ELM) and VMD-based least squares support vector regression (VMD-LSSVR). The VMD is employed to decompose original input and target time series into sub-time series called intrinsic mode functions (IMFs). The ELM and LSSVR models are selected for developing daily rainfall-runoff models utilizing the IMFs as inputs. The performances of VMD-ELM and VMD-LSSVR models are evaluated utilizing efficiency and effectiveness indices. Their performances are also compared with those of VMD-based artificial neural network (VMD-ANN), discrete wavelet transform (DWT)-based MLMs (DWT-ELM, DWT-LSSVR, and DWT-ANN) and single MLMs (ELM, LSSVR, and ANN). As a result, the VMD-based MLMs provide better accuracy compared with the single MLMs and yield slightly better performance than the DWT-based MLMs. Among all models, the VMD-ELM and VMD-LSSVR models achieve the best performance in daily rainfall-runoff modeling with respect to efficiency and effectiveness. Therefore, the VMD-ELM and VMD-LSSVR models can be an alternative tool for reliable and accurate daily rainfall-runoff modeling.

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“…The tradeoff problem can be resolved by performing a fine tuning by the DSS. Thus, the coupled CSA and DSS result in better performance and more optimal solutions [97,98].…”

Section: Models Ds and As Input Variables Output Variablesmentioning

confidence: 99%

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

2018

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“…In this work the LS‐SVM models were developed using Matlab software (version 2011a) and the toolbox StatLSSVM (De Brabanter, Suykens, & De Moor, ).…”

Section: Methodsmentioning

confidence: 99%

A rapid flood inundation model for hazard mapping based on least squares support vector machine regression

Bermúdez

Cea

Puertas

2019

J Flood Risk Management

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Two-dimensional shallow water models are widely used tools for flood inundation mapping. However, even if High Performance Computing techniques have greatly decreased the computational time needed to run a 2D inundation model, this approach remains unsuitable for applications that require results in a very short time or a large number of model runs. In this paper we test a non-parametric regression model based on least squares support vector machines as a computationally efficient surrogate of the 2D shallow water equations for flood inundation mapping. The methodology is initially applied to a synthetic case study consisting of a straight river reach flowing towards the sea. A coastal urban area is then used as a real test case. Discharge in three streams and tide levels are used as predictor variables to estimate the spatial distribution of maximum water depth and velocity in the study area. The suitability of this regression model for the spatial prediction of flood hazard is evaluated. The results show the potential of the proposed regression technique for fast and accurate computation of flood extent and hazard maps. K E Y W O R D S flood hazard, flood inundation, Iber model, shallow water equations, support vector machine 1 | INTRODUCTIONPhysically-based flood inundation models are widely used tools for simulating river hydraulics and floodplain inundation processes. In these models, the flood propagation is generally described by the two-dimensional shallow water equations (2D-SWEs), which must be solved using an appropriate numerical technique. Models solve either the full or simplified forms of these equations, as the local inertial approximation or the diffusive wave approximation (Neal et al., 2012). In practice, an inevitable compromise between accuracy and efficiency must be found when defining the spatial resolution of the numerical mesh and the time discretization (Chen et al., 2012). Higher accuracy can be obtained by increasing the physical complexity and the spatial and temporal resolution of a model, but at the cost of higher computation time.This conventional physically-based approach is hence generally impractical for applications that require results in a very short time or a large number of model runs. A typical example is a probabilistic analysis such as the propagation of uncertainty in a Monte Carlo context, which involves the evaluation of thousands model runs. A few Monte Carlo probabilistic analyses using 2D-SWE models have been presented in the literature but the number of runs is generally limited to a few hundreds (Cea, Bermúdez, & Puertas, 2011;Fraga et al., 2016), which limits the number of parameters that can be analysed and the statistical significance of the results. Real-time forecasting systems are another example where fast computation is imperative to issue flood warnings with sufficient lead-time. In these kinds of applications, a possible way to reduce significantly the computation time is the development of computationally more efficient surrogates of these models. A fi...

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“…For the LS-SVM, there was no standalone freeware to our knowledge. Available MATLAB toolboxes, such as LS-SVMlab [11] and StatLSSVM [21], had problems with our large CO 2 dataset. We have written a standalone freeware available at http://united-csfe.com/fcew/ann.zip.…”

Section: Softwarementioning

confidence: 99%

“…Unless a software includes parameter tuning tools, implementing methods is not a simple task for many users. Meanwhile, an automatic tuning tool, for example the MATLAB toolbox of [11,21], may not work with a large dataset or may take a very long time to compute.…”

Section: Introductionmentioning

confidence: 99%

Generalization of Parameter Selection of SVM and LS-SVM for Regression

Zeng

Tan

Matsunaga

et al. 2019

MAKE

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A Support Vector Machine (SVM) for regression is a popular machine learning model that aims to solve nonlinear function approximation problems wherein explicit model equations are difficult to formulate. The performance of an SVM depends largely on the selection of its parameters. Choosing between an SVM that solves an optimization problem with inequality constrains and one that solves the least square of errors (LS-SVM) adds to the complexity. Various methods have been proposed for tuning parameters, but no article puts the SVM and LS-SVM side by side to discuss the issue using a large dataset from the real world, which could be problematic for existing parameter tuning methods. We investigated both the SVM and LS-SVM with an artificial dataset and a dataset of more than 200,000 points used for the reconstruction of the global surface ocean CO2 concentration. The results reveal that: (1) the two models are most sensitive to the parameter of the kernel function, which lies in a narrow range for scaled input data; (2) the optimal values of other parameters do not change much for different datasets; and (3) the LS-SVM performs better than the SVM in general. The LS-SVM is recommended, as it has less parameters to be tuned and yields a smaller bias. Nevertheless, the SVM has advantages of consuming less computer resources and taking less time to train. The results suggest initial parameter guesses for using the models.

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Nonparametric Regression viaStatLSSVM

Cited by 24 publications

References 74 publications

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

A rapid flood inundation model for hazard mapping based on least squares support vector machine regression

Generalization of Parameter Selection of SVM and LS-SVM for Regression

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