The prediction model of earthquake casuailty based on robust wavelet v-SVM

Huang Xing (), Zhou Zhonglin and Wang Shaoyu

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2015, vol. 77, issue 2, 717-732

Abstract: Prediction of earthquake casualty is fundamental to effectively determine the amount of emergency supply collected and to track the variation in emergency supply demand in time. Upon the lack of information for predicting casualties in the earlier stage of earthquake and due to the features of predictors as small sample and nonlinearity, this paper applies improved support vector machine (SVM) to the construction of earthquake casualty prediction model and proposes robust wavelet (RW) v-SVM earthquake casualty prediction model. Considering the disadvantage that a single loss function in SVM is not able to suppress the large amplitudes and singular points of earthquake casualty predictors, a robust loss function that allows for segmented suppression is designed by combining with Gaussian loss function, Laplace loss function and ρ-insensitive loss function, to handle different data of casualty predictors. In order to minimize the nonlinear classification error of SVM in higher-dimensional space, the independent variables in both the Morlet and Mexican parent wavelet kernel functions are replaced with a wavelet kernel function satisfying Mercer translation invariant kernel; thus, two wavelet kernel functions are obtained for machine learning, to mitigate the limitation of normal kernel function as reducing the errors. The numerical example shows that RW v-SVM model features rapid learning, high-precision prediction and advanced stability when it is used to predict earthquake casualties, which provides an effective method to solve this problem. Copyright Springer Science+Business Media Dordrecht 2015

Keywords: Earthquake casualty prediction; Model construction; RW v-SVM; Loss function (search for similar items in EconPapers)
Date: 2015
References: View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://hdl.handle.net/10.1007/s11069-015-1620-2 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:77:y:2015:i:2:p:717-732

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-015-1620-2

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().