Standard

A soft-computing ensemble approach (SEA) to forecast Indian summer monsoon rainfall. / Kurian, Nisha; Venugopal, T.; Singh, Jatin et al.

In: Meteorological Applications, Vol. 24, No. 2, 01.04.2017, p. 308-314.

Research output: Contribution to journal › Article › peer-review

Harvard

Kurian, N, Venugopal, T, Singh, J & Ali, MM 2017, 'A soft-computing ensemble approach (SEA) to forecast Indian summer monsoon rainfall', Meteorological Applications, vol. 24, no. 2, pp. 308-314. https://doi.org/10.1002/met.1650

APA

Kurian, N., Venugopal, T., Singh, J., & Ali, M. M. (2017). A soft-computing ensemble approach (SEA) to forecast Indian summer monsoon rainfall. Meteorological Applications, 24(2), 308-314. https://doi.org/10.1002/met.1650

Vancouver

Kurian N, Venugopal T, Singh J, Ali MM. A soft-computing ensemble approach (SEA) to forecast Indian summer monsoon rainfall. Meteorological Applications. 2017 Apr 1;24(2):308-314. doi: 10.1002/met.1650

Author

Kurian, Nisha ; Venugopal, T. ; Singh, Jatin et al. / A soft-computing ensemble approach (SEA) to forecast Indian summer monsoon rainfall. In: Meteorological Applications. 2017 ; Vol. 24, No. 2. pp. 308-314.

BibTeX

@article{2d2108a13e8e4158b52944e3dddbbae0,
title = "A soft-computing ensemble approach (SEA) to forecast Indian summer monsoon rainfall",
abstract = "Agriculture is the backbone of the Indian economy and contributes ∼16% of gross domestic product and about 10% of total exports. Hence, accurate and timely forecasting of monthly Indian 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 Indian subcontinent. Forecasts using January to May initial conditions along with observations during 1982–2014 were used to develop the model. The SEA compares well with observations.",
keywords = "ensemble, monsoon forecasting, neural networks, TO-INTERANNUAL PREDICTION, PREDICTABILITY, WEATHER, SURFACE, NEURAL-NETWORK, INTRASEASONAL OSCILLATIONS, RADIATION",
author = "Nisha Kurian and T. Venugopal and Jatin Singh and Ali, {M. M.}",
note = "Publisher Copyright: {\textcopyright} 2017 Royal Meteorological Society",
year = "2017",
month = apr,
day = "1",
doi = "10.1002/met.1650",
language = "English",
volume = "24",
pages = "308--314",
journal = "Meteorological Applications",
issn = "1350-4827",
publisher = "Wiley-Blackwell",
number = "2",

}

RIS

TY - JOUR

T1 - A soft-computing ensemble approach (SEA) to forecast Indian summer monsoon rainfall

AU - Kurian, Nisha

AU - Venugopal, T.

AU - Singh, Jatin

AU - Ali, M. M.

N1 - Publisher Copyright: © 2017 Royal Meteorological Society

PY - 2017/4/1

Y1 - 2017/4/1

N2 - Agriculture is the backbone of the Indian economy and contributes ∼16% of gross domestic product and about 10% of total exports. Hence, accurate and timely forecasting of monthly Indian 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 Indian subcontinent. Forecasts using January to May initial conditions along with observations during 1982–2014 were used to develop the model. The SEA compares well with observations.

AB - Agriculture is the backbone of the Indian economy and contributes ∼16% of gross domestic product and about 10% of total exports. Hence, accurate and timely forecasting of monthly Indian 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 Indian subcontinent. Forecasts using January to May initial conditions along with observations during 1982–2014 were used to develop the model. The SEA compares well with observations.

KW - ensemble

KW - monsoon forecasting

KW - neural networks

KW - TO-INTERANNUAL PREDICTION

KW - PREDICTABILITY

KW - WEATHER

KW - SURFACE

KW - NEURAL-NETWORK

KW - INTRASEASONAL OSCILLATIONS

KW - RADIATION

UR - http://www.scopus.com/inward/record.url?scp=85014592509&partnerID=8YFLogxK

U2 - 10.1002/met.1650

DO - 10.1002/met.1650

M3 - Article

AN - SCOPUS:85014592509

VL - 24

SP - 308

EP - 314

JO - Meteorological Applications

JF - Meteorological Applications

SN - 1350-4827

IS - 2

ER -

ID: 10039948