Open AccessA soft‐computing ensemble approach ( SEA ) to forecast I ndian summer monsoon rainfall
Author(s) -
Kurian Nisha,
Venugopal T.,
Singh Jatin,
Ali M. M.
Publication year - 2017
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1002/met.1650
Subject(s) - indian subcontinent , monsoon , environmental science , meteorology , agriculture , climatology , product (mathematics) , computer science , mathematics , geography , geology , ancient history , geometry , archaeology , history
Agriculture is the backbone of the I ndian economy and contributes ∼16% of gross domestic product and about 10% of total exports. Hence, accurate and timely forecasting of monthly I ndian summer monsoon rainfall is very much in demand for economic planning and agricultural practices. Several methods and models, comprising dynamic and statistical models and combinations of the two, exist for monsoon forecasting. Here, a multi‐model ensemble approach, combined with an artificial neural networking technique, was used to develop a soft‐computing ensemble algorithm ( SEA ) to forecast the monthly and seasonal rainfall over the I ndian subcontinent. Forecasts using J anuary to M ay initial conditions along with observations during 1982–2014 were used to develop the model. The SEA compares well with observations.