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Article

Snow Disaster Early Warning in Pastoral Areas of Qinghai Province, China

1
State Key Laboratory of Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China
2
Laboratory for Remote Sensing and Geoinformatics, University of Texas at San Antonio, San Antonio, TX 78249, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Claudia Notarnicola, Soe Myint and Prasad Thenkabail
Remote Sens. 2017, 9(5), 475; https://doi.org/10.3390/rs9050475
Received: 15 March 2017 / Revised: 4 May 2017 / Accepted: 9 May 2017 / Published: 12 May 2017
(This article belongs to the Special Issue Snow Remote Sensing)
It is important to predict snow disasters to prevent and reduce hazards in pastoral areas. In this study, we build a potential risk assessment model based on a logistic regression of 33 snow disaster events that occurred in Qinghai Province. A simulation model of the snow disaster early warning is established using a back propagation artificial neural network (BP-ANN) method and is then validated. The results show: (1) the potential risk of a snow disaster in the Qinghai Province is mainly determined by five factors. Three factors are positively associated, the maximum snow depth, snow-covered days (SCDs), and slope, and two are negative factors, annual mean temperature and per capita gross domestic product (GDP); (2) the key factors that contribute to the prediction of a snow disaster are (from the largest to smallest contribution): the mean temperature, probability of a spring snow disaster, potential risk of a snow disaster, continual days of a mean daily temperature below −5 °C, and fractional snow-covered area; and (3) the BP-ANN model for an early warning of snow disaster is a practicable predictive method with an overall accuracy of 80%. This model has quite a few advantages over previously published models, such as it is raster-based, has a high resolution, and has an ideal capacity of generalization and prediction. The model output not only tells which county has a disaster (published models can) but also tells where and the degree of damage at a 500 m pixel scale resolution (published models cannot). View Full-Text
Keywords: snow disaster; risk assessment; early warning; artificial neural network; pastoral area snow disaster; risk assessment; early warning; artificial neural network; pastoral area
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MDPI and ACS Style

Gao, J.; Huang, X.; Ma, X.; Feng, Q.; Liang, T.; Xie, H. Snow Disaster Early Warning in Pastoral Areas of Qinghai Province, China. Remote Sens. 2017, 9, 475. https://doi.org/10.3390/rs9050475

AMA Style

Gao J, Huang X, Ma X, Feng Q, Liang T, Xie H. Snow Disaster Early Warning in Pastoral Areas of Qinghai Province, China. Remote Sensing. 2017; 9(5):475. https://doi.org/10.3390/rs9050475

Chicago/Turabian Style

Gao, Jinlong, Xiaodong Huang, Xiaofang Ma, Qisheng Feng, Tiangang Liang, and Hongjie Xie. 2017. "Snow Disaster Early Warning in Pastoral Areas of Qinghai Province, China" Remote Sensing 9, no. 5: 475. https://doi.org/10.3390/rs9050475

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