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| Titel |
Application of GA–SVM method with parameter optimization for landslide development prediction |
| VerfasserIn |
X. Z. Li, J. M. Kong |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Sciences ; 14, no. 3 ; Nr. 14, no. 3 (2014-03-04), S.525-533 |
| Datensatznummer |
250118329
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| Publikation (Nr.) |
 copernicus.org/nhess-14-525-2014.pdf |
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| Zusammenfassung |
| Prediction of the landslide development process is always a hot issue in
landslide research. So far, many methods for landslide displacement series
prediction have been proposed. The support vector machine (SVM) has been
proved to be a novel algorithm with good performance. However, the
performance strongly depends on the right selection of the parameters (C
and γ) of the SVM model. In this study, we present an application of
genetic algorithm and support vector machine (GA–SVM) method with parameter
optimization in landslide displacement rate prediction. We selected a typical
large-scale landslide in a hydro-electrical engineering area of southwest
China as a case. On the basis of analyzing the basic characteristics and
monitoring data of the landslide, a single-factor GA–SVM model and a
multi-factor GA–SVM model of the landslide were built. Moreover, the models
were compared with single-factor and multi-factor SVM models of the
landslide. The results show that the four models have high prediction
accuracies, but the accuracies of GA–SVM models are slightly higher than
those of SVM models, and the accuracies of multi-factor models are slightly
higher than those of single-factor models for the landslide prediction. The
accuracy of the multi-factor GA–SVM models is the highest, with the smallest
root mean square error (RMSE) of 0.0009 and the highest relation index (RI)
of 0.9992. |
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