Abstract
According to disaster and risk evaluation theory, we proposed an indicator system containing environmental possibilities with hazard, disaster inducing factors and disaster bearing bodies to analyze the risk of heavy snow disaster in Xilingol, Inner Mongolia, based on the analysis of heavy snow events that have occurred in the last several decades. A risk evaluation model of heavy snow disaster was established using back-propagation artificial neural network (BP-ANN). Data obtained from a number of heavy snow events samples were used to train artificial neural network (ANN). The objective of this study is to produce a new evaluation model using BP-ANN for heavy snow risk analysis. As a result, BP-ANN model showed an advantage in heavy snow risk evaluation in Xilingol compared to the conventional method of evaluation criteria equation (ECE) introduced by Inner Mongolia Municipality Animal Husbandry Bureau. Thus, the BP-ANN model provides an alternative method for heavy snow risk analysis in the area.
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Abbreviations
- ANN:
-
Artificial neural network
- BP-ANN:
-
Back-propagation artificial neural network
- RMSE:
-
Root mean square error
- ECE:
-
Evaluation criteria equation of heavy snow
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Acknowledgments
This work was funded by BNSFC-8062020, National Technological Support Projects-2006BAD20B01 and 111 Project; the authors greatly appreciate their supports. Authors also thank anonymous reviewers for helpful comments and suggestions.
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Wu, Jd., Li, N., Yang, Hj. et al. Risk evaluation of heavy snow disasters using BP artificial neural network: the case of Xilingol in Inner Mongolia. Stoch Environ Res Risk Assess 22, 719–725 (2008). https://doi.org/10.1007/s00477-007-0181-7
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DOI: https://doi.org/10.1007/s00477-007-0181-7