Application of GA–SVM method with parameter optimization for landslide development prediction
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2014-03-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | http://www.nat-hazards-earth-syst-sci.net/14/525/2014/nhess-14-525-2014.pdf |
| _version_ | 1818482888124923904 |
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| author | X. Z. Li J. M. Kong |
| author_facet | X. Z. Li J. M. Kong |
| author_sort | X. Z. Li |
| collection | DOAJ |
| description | 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 (<i>C</i>
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. |
| first_indexed | 2024-12-10T11:52:40Z |
| format | Article |
| id | doaj.art-a8af529abed9471da172b74eca3f8558 |
| institution | Directory Open Access Journal |
| issn | 1561-8633 1684-9981 |
| language | English |
| last_indexed | 2024-12-10T11:52:40Z |
| publishDate | 2014-03-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Natural Hazards and Earth System Sciences |
| spelling | doaj.art-a8af529abed9471da172b74eca3f85582022-12-22T01:49:53ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812014-03-0114352553310.5194/nhess-14-525-2014Application of GA–SVM method with parameter optimization for landslide development predictionX. Z. Li0J. M. Kong1Key Laboratory of Mountain Hazards and Surface Process, Chinese Academy of Sciences, 610041, Chengdu, ChinaKey Laboratory of Mountain Hazards and Surface Process, Chinese Academy of Sciences, 610041, Chengdu, ChinaPrediction 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 (<i>C</i> 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.http://www.nat-hazards-earth-syst-sci.net/14/525/2014/nhess-14-525-2014.pdf |
| spellingShingle | X. Z. Li J. M. Kong Application of GA–SVM method with parameter optimization for landslide development prediction Natural Hazards and Earth System Sciences |
| title | Application of GA–SVM method with parameter optimization for landslide development prediction |
| title_full | Application of GA–SVM method with parameter optimization for landslide development prediction |
| title_fullStr | Application of GA–SVM method with parameter optimization for landslide development prediction |
| title_full_unstemmed | Application of GA–SVM method with parameter optimization for landslide development prediction |
| title_short | Application of GA–SVM method with parameter optimization for landslide development prediction |
| title_sort | application of ga svm method with parameter optimization for landslide development prediction |
| url | http://www.nat-hazards-earth-syst-sci.net/14/525/2014/nhess-14-525-2014.pdf |
| work_keys_str_mv | AT xzli applicationofgasvmmethodwithparameteroptimizationforlandslidedevelopmentprediction AT jmkong applicationofgasvmmethodwithparameteroptimizationforlandslidedevelopmentprediction |