Journal cover Journal topic
Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
doi:10.5194/nhessd-3-5957-2015
© Author(s) 2015. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
02 Oct 2015
Review status
This discussion paper has been under review for the journal Natural Hazards and Earth System Sciences (NHESS). The revised manuscript was not accepted.
Setting up the critical rainfall line for debris flows via support vector machines
Y. F. Tsai1, C. H. Chan2, and C. H. Chang3 1Department of Social and Regional Development, National Taipei University of Education, Taipei, Taiwan
2Department of Geography, National Taiwan University, Taipei, Taiwan
3Department of Civil Engineering, Tamkang University, New Taipei City, Taiwan
Abstract. The Chi-Chi earthquake in 1999 caused tremendous landslides which triggered many debris flows and resulted in significant loss of public lives and property. To prevent the disaster of debris flow, setting a critical rainfall line for each debris-flow stream is necessary. Firstly, 8 predisposing factors of debris flow were used to cluster 377 streams which have similar rainfall lines into 7 groups via the genetic algorithm. Then, support vector machines (SVM) were applied to setup the critical rainfall line for debris flows. SVM is a machine learning approach proposed based on statistical learning theory and has been widely used on pattern recognition and regression. This theory raises the generalized ability of learning mechanisms according to the minimum structural risk. Therefore, the advantage of using SVM can obtain results of minimized error rates without many training samples. Finally, the experimental results confirm that SVM method performs well in setting a critical rainfall line for each group of debris-flow streams.

Citation: Tsai, Y. F., Chan, C. H., and Chang, C. H.: Setting up the critical rainfall line for debris flows via support vector machines, Nat. Hazards Earth Syst. Sci. Discuss., 3, 5957-5975, doi:10.5194/nhessd-3-5957-2015, 2015.
Y. F. Tsai et al.
Y. F. Tsai et al.

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Firstly, 8 predisposing factors of debris flow were used to cluster 377 streams which have similar rainfall lines into 7 groups via the genetic algorithm. Then, support vector machines (SVM) were applied to setup the critical rainfall line for debris flows.
Firstly, 8 predisposing factors of debris flow were used to cluster 377 streams which have...
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