Your browser doesn't support javascript.
loading
Artificial intelligence predicts normal summer monsoon rainfall for India in 2023.
Narang, Udit; Juneja, Kushal; Upadhyaya, Pankaj; Salunke, Popat; Chakraborty, Tanmoy; Behera, Swadhin Kumar; Mishra, Saroj Kanta; Suresh, Akhil Dev.
Afiliação
  • Narang U; Department of Computer Science and Engineering, Indraprastha Institute of Information Technology Delhi, Delhi, India.
  • Juneja K; Department of Computer Science and Engineering, Indraprastha Institute of Information Technology Delhi, Delhi, India.
  • Upadhyaya P; Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, Delhi, India.
  • Salunke P; Center for Global Change Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
  • Chakraborty T; Department of Electrical Engineering, Indian Institute of Technology Delhi, Delhi, India. tanchak@iitd.ac.in.
  • Behera SK; Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan.
  • Mishra SK; Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, Delhi, India. skm@iitd.ac.in.
  • Suresh AD; Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, Delhi, India.
Sci Rep ; 14(1): 1495, 2024 Jan 17.
Article em En | MEDLINE | ID: mdl-38233406
ABSTRACT
Inaccuracy in the All Indian Summer Monsoon Rainfall (AISMR) forecast has major repercussions for India's economy and people's daily lives. Improving the accuracy of AISMR forecasts remains a challenge. An attempt is made here to address this problem by taking advantage of recent advances in machine learning techniques. The data-driven models trained with historical AISMR data, the Niño3.4 index, and categorical Indian Ocean Dipole values outperform the traditional physical models, and the best-performing model predicts that the 2023 AISMR will be roughly 790 mm, which is typical of a normal monsoon year.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sci Rep Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Índia País de publicação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sci Rep Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Índia País de publicação: Reino Unido