globalchange  > 过去全球变化的重建
DOI: 10.1007/s00382-013-1942-2
Scopus记录号: 2-s2.0-84906777236
论文题名:
Development of an artificial neural network based multi-model ensemble to estimate the northeast monsoon rainfall over south peninsular India: An application of extreme learning machine
作者: Acharya N.; Shrivastava N.A.; Panigrahi B.K.; Mohanty U.C.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2014
卷: 43, 期:2017-05-06
起始页码: 1303
结束页码: 1310
语种: 英语
英文关键词: Artificial neural network ; Extreme learning machine ; General circulation models ; Multi-model ensemble ; Northeast monsoon rainfall
英文摘要: The south peninsular part of India gets maximum amount of rainfall during the northeast monsoon (NEM) season [October to November (OND)] which is the primary source of water for the agricultural activities in this region. A nonlinear method viz., Extreme learning machine (ELM) has been employed on general circulation model (GCM) products to make the multi-model ensemble (MME) based estimation of NEM rainfall (NEMR). The ELM is basically is an improved learning algorithm for the single feed-forward neural network (SLFN) architecture. The 27 year (1982-2008) lead-1 (using initial conditions of September for forecasting the mean rainfall of OND) hindcast runs (1982-2008) from seven GCM has been used to make MME. The improvement of the proposed method with respect to other regular MME (simple arithmetic mean of GCMs (EM) and singular value decomposition based multiple linear regressions based MME) has been assessed through several skill metrics like Spread distribution, multiplicative bias, prediction errors, the yield of prediction, Pearson's and Kendal's correlation coefficient and Wilmort's index of agreement. The efficiency of ELM estimated rainfall is established by all the stated skill scores. The performance of ELM in extreme NEMR years, out of which 4 years are characterized by deficit rainfall and 5 years are identified as excess, is also examined. It is found that the ELM could expeditiously capture these extremes reasonably well as compared to the other MME approaches. © 2013 Springer-Verlag Berlin Heidelberg.
资助项目: NCAR, National Oceanic and Atmospheric Administration ; NOAA, National Oceanic and Atmospheric Administration
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/54661
Appears in Collections:过去全球变化的重建

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作者单位: Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, New Delhi, 110016, India; Electrical Engineering Departments, Indian Institute of Technology Delhi, New Delhi, India; School of Earth, Ocean and Climate Sciences, Indian Institute of Technology, Bhubaneswar, Odisha, India

Recommended Citation:
Acharya N.,Shrivastava N.A.,Panigrahi B.K.,et al. Development of an artificial neural network based multi-model ensemble to estimate the northeast monsoon rainfall over south peninsular India: An application of extreme learning machine[J]. Climate Dynamics,2014-01-01,43(2017-05-06)
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