Fixed-Size LS-SVM Applied to the Wiener-Hammerstein Benchmark

Brabanter, Kris De; Dreesen, Ph.; Karsmakers, Peter; Pelckmans, Kristiaan; Brabanter, Jos De; Suykens, Johan A. K.; Moor, Bart De

doi:10.3182/20090706-3-fr-2004.00137

Cited by 52 publications

(43 citation statements)

References 7 publications

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“…This study considered fuel load as the foliage biomass, computed using a non-linear regularized Least-Squares Support Vector Machine (LS-SVM) as implemented in the LS-SVMlab Toolbox developed by De Brabanter et al [50] for Matlab [51]. This approach attempts to find the optimal loss function by solving a set of linear equations as an approximation for the quadratic programming problem, reducing the high computational burden associated with solving the quadratic programming problem.…”

Section: Canopy Fuel Properties Estimation From Lidar Datamentioning

confidence: 99%

Extrapolating Forest Canopy Fuel Properties in the California Rim Fire by Combining Airborne LiDAR and Landsat OLI Data

et al. 2017

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Accurate, spatially explicit information about forest canopy fuel properties is essential for ecosystem management strategies for reducing the severity of forest fires. Airborne LiDAR technology has demonstrated its ability to accurately map canopy fuels. However, its geographical and temporal coverage is limited, thus making it difficult to characterize fuel properties over large regions before catastrophic events occur. This study presents a two-step methodology for integrating post-fire airborne LiDAR and pre-fire Landsat OLI (Operational Land Imager) data to estimate important pre-fire canopy fuel properties for crown fire spread, namely canopy fuel load (CFL), canopy cover (CC), and canopy bulk density (CBD). This study focused on a fire prone area affected by the large 2013 Rim fire in the Sierra Nevada Mountains, California, USA. First, LiDAR data was used to estimate CFL, CC, and CBD across an unburned 2 km buffer with similar structural characteristics to the burned area. Second, the LiDAR-based canopy fuel properties were extrapolated over the whole area using Landsat OLI data, which yielded an R 2 of 0.8, 0.79, and 0.64 and RMSE of 3.76 Mg· ha −1 , 0.09, and 0.02 kg·m −3 for CFL, CC, and CBD, respectively. The uncertainty of the estimates was estimated for each pixel using a bootstrapping approach, and the 95% confidence intervals are reported. The proposed methodology provides a detailed spatial estimation of forest canopy fuel properties along with their uncertainty that can be readily integrated into fire behavior and fire effects models. The methodology could be also integrated into the LANDFIRE program to improve the information on canopy fuels.

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Section: Canopy Fuel Properties Estimation From Lidar Datamentioning

confidence: 99%

Extrapolating Forest Canopy Fuel Properties in the California Rim Fire by Combining Airborne LiDAR and Landsat OLI Data

et al. 2017

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“…Finally we compare these methods with the support vector machine (SVM) [7] and multilayer perceptrons with gradient learning algorithm (MLP) [19]. We use three types In order to work well, the input recursive vector is modified as X(k) = [y(k − 1) · · · y(k − 10) u(k) · · · u(k − 5)] T .…”

Section: Wiener-hammerstein Benchmarkmentioning

confidence: 99%

Randomized algorithms for nonlinear system identification with deep learning modification

Rosa

2016

Information Sciences

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“…Three kinds of kernel functions, linear kernel, multilayer kernel, and radial basis function (RBF) kernel, are normally used by researchers [7,31]. In this work, the RBF kernel function is used since it gives excellent generalization and a low computational cost.…”

Section: Consider a Training Dataset {Xmentioning

confidence: 99%

“…In this work, SVM is used as a classifier and it is a promising method for solving nonlinear classification problems, function and density estimation, and pattern recognition tasks [7,31]. It was originally proposed to classify samples within 2 classes.…”

Section: Support Vector Machinementioning

confidence: 99%

Comparison of speech parameterization techniques for the classification of speech disfluencies

Fook¹,

Hariharan²,

Chee³

et al. 2013

Turk J Elec Eng & Comp Sci

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Fixed-Size LS-SVM Applied to the Wiener-Hammerstein Benchmark

Cited by 52 publications

References 7 publications

Extrapolating Forest Canopy Fuel Properties in the California Rim Fire by Combining Airborne LiDAR and Landsat OLI Data

Extrapolating Forest Canopy Fuel Properties in the California Rim Fire by Combining Airborne LiDAR and Landsat OLI Data

Randomized algorithms for nonlinear system identification with deep learning modification

Comparison of speech parameterization techniques for the classification of speech disfluencies

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