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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 |
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| Authors: | Nachiketa Acharya Nitin Anand Shrivastava B K Panigrahi U C Mohanty |
| Institution: | 1. Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, New Delhi, 110016, India 3. School of Earth, Ocean and Climate Sciences, Indian Institute of Technology, Bhubaneswar, Odisha, India 2. Electrical Engineering Departments, Indian Institute of Technology Delhi, New Delhi, India |
| Abstract: | 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. |
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