Evidence map›Paper›PMID 38233406›Full record

ArticleScientific reports2024

Artificial intelligence predicts normal summer monsoon rainfall for India in 2023.

Udit Narang, Kushal Juneja, Pankaj Upadhyaya, Popat Salunke, Tanmoy Chakraborty, Swadhin Kumar Behera, Saroj Kanta Mishra, Akhil Dev Suresh

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PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Udit NarangDepartment of Computer Science and Engineering, Indraprastha Institute of Information Technology Delhi, Delhi, India.
Kushal JunejaDepartment of Computer Science and Engineering, Indraprastha Institute of Information Technology Delhi, Delhi, India.
Pankaj UpadhyayaCentre for Atmospheric Sciences, Indian Institute of Technology Delhi, Delhi, India.
Popat SalunkeCenter for Global Change Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Tanmoy ChakrabortyDepartment of Electrical Engineering, Indian Institute of Technology Delhi, Delhi, India. tanchak@iitd.ac.in.
Swadhin Kumar BeheraApplication Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan.
Saroj Kanta MishraCentre for Atmospheric Sciences, Indian Institute of Technology Delhi, Delhi, India. skm@iitd.ac.in.
Akhil Dev SureshCentre for Atmospheric Sciences, Indian Institute of Technology Delhi, Delhi, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID38233406
PMCPMC10794699

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