Abstract
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).
Highlights
Snow disasters in pastoral regions are meteorological disasters that affect animal husbandry because of heavy snow, sustained low temperatures, and prolonged snow cover
We first construct a logistic regression model to evaluate the potential risk associated with a snow disaster and calculate the potential snow disaster risk using a correlation analysis, principal component analysis (PCA), and other methods based on 33 typical snow disaster cases from 2001 to 2007 in Qinghai Province
Based on a risk analysis and the factors that influence snow disasters in pastoral areas of the Qinghai Province, a snow disaster model for early warning based on the back propagation artificial neural network (BP-ANN) machine learning method at a 500 m spatial resolution is developed and validated
Summary
Snow disasters in pastoral regions are meteorological disasters that affect animal husbandry because of heavy snow, sustained low temperatures, and prolonged snow cover. Province is located on the northeastern part of the Tibetan Plateau and often receives heavy snowfall in the winter and spring because of the influence of the plateau’s specific geographic environment and climatic conditions. These conditions threaten the personal safety of herdsmen and their personal properties. Remote Sens. 2017, 9, 475; doi:10.3390/rs9050475 www.mdpi.com/journal/remotesensing
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
