Hidden Markov Model for quantitative prediction of snowfall and analysis of hazardous snowfall events over Indian Himalaya
作者: J C JOSHIK TANKESHWARSunita SRIVASTAVA
作者单位: 1Snow and Avalanche Study Establishment
2Panjab University
刊名: Journal of Earth System Science, 2017, Vol.126 (3)
来源数据库: Springer Nature Journal
DOI: 10.1007/s12040-017-0810-6
关键词: Precipitation forecastForward algorithmViterbi algorithmBaum–Welch algorithm
原始语种摘要: A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992–2012. There are six observations and six states of the model. The most probable observation and state sequence has been computed using Forward and Viterbi algorithms, respectively. Baum–Welch algorithm has been used for optimizing the model parameters. The model has been validated for two winters (2012–2013 and 2013–2014) by computing root mean square error (RMSE), accuracy measures such as percent correct (PC), critical success index (CSI) and Heidke skill score (HSS). The RMSE of the model has also been...
全文获取路径: Springer Nature  (合作)
分享到:
来源刊物:
影响因子:0.695 (2012)

×
关键词翻译
关键词翻译
  • forecast 预报
  • snowfall 降雪量
  • algorithm 算法
  • hazardous 危险
  • model 模型
  • quantitative 量的
  • prediction 预报
  • events 定时
  • percent 百分率
  • deviation 偏差