IIT Delhi researchers develop ML-based model for forecasting monsoon rainfall

IIT Delhi researchers

In collaboration with IIT Delhi, MIT in the United States, and JAMSTEC in Japan, researchers at the DST Centre of Excellence in Climate Modelling have created a cutting-edge machine-learning model for forecasting monsoon rainfall. A typical monsoon is defined as having an All India Summer Monsoon Rainfall (AISMR) of roughly 790mm in 2023, according to the model.

According to a statement from the institute, the model has exceeded the country’s present physical models. For the test period between 2002 and 2022, the machine learning model was able to attain a predicted success rate of 61.9%. The model was supplied with AISMR data, Nio3.4 index data, and categorical Indian Ocean Dipole (IOD) data for the years 1901–2001 in order to make predictions.

This model can forecast the monsoon based on the data that is available. Then, it can be modified to reflect evolving situations. Aside from making inputs more flexible, it requires less calculation.

“This study holds immense significance for the entire nation,” said Prof. Saroj K. Mishra, Principal Investigator, DST Centre of Excellence in Climate Modelling and professor at the Centre for Atmospheric Sciences, IIT Delhi. “An accurate monsoon forecast well in advance is pivotal for making crucial decisions in various socioeconomic sectors, including agriculture, energy, water resources, disaster management, and health.”

The prediction and forecasting of the weather is becoming more and more reliant on AI approaches. A predictor discovery algorithm (PDA) was created in April by a group of scientists from Cotton University in Guwahati and the Indian Institute of Tropical Meteorology (IITM), Pune. The system can produce accurate monsoon predictions 18 months in advance of the season, according to the Ministry of Science and Technology.

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