Setting up the critical rainfall line for debris flows via support vector machines
Y. F. Tsai1, C. H. Chan2, and C. H. Chang31Department 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
Received: 18 Aug 2015 – Accepted for review: 06 Sep 2015 – Discussion started: 02 Oct 2015
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.
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.