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IIT Delhi's AI/ML model predicts 2023 to be normal monsoon year
India will see a normal monsoon in 2023, according to a new machine learning model, developed by an international team of researchers led by Indian Institutes of Technology (IIT) Delhi.
New Delhi: India will see a normal monsoon in 2023, according to a new machine learning model, developed by an international team of researchers led by Indian Institutes of Technology (IIT) Delhi.
The model predicted an All India Summer Monsoon Rainfall (AISMR) of about 790 mm in the upcoming monsoon season, meaning a normal monsoon.
It demonstrated a remarkable forecast success rate of 61.9 per cent for the test period of 2002-2022. This is based on whether the model is able to predict the AISMR within plus or minus 5 per cent of the actual values observed each year.
"This study holds immense significance for the entire country, as an accurate monsoon forecast well ahead of time is pivotal for making crucial decisions in various socioeconomic sectors, including agriculture, energy, water resources, disaster management, and health," said Prof. Saroj K. Mishra, of the DST Centre of Excellence in Climate Modeling and Professor at the Centre for Atmospheric Sciences, IIT Delhi.
Prof. Mishra added that the data-driven techniques will be extended to provide state-wise monsoon rainfall prediction, making them more useful for regional applications.
The AI/ML model has proven to be better performing than the current physical models used for monsoon predictions in the country.
The prediction using the AI/ML model can be made months in advance, depending on the availability of Nino3.4 index and Indian Ocean Dipole forecast, and can be updated accordingly based on their evolution.
Thus, the data-driven models are flexible to inputs and can better capture the nonlinear relations among the monsoon drivers, all while being less computationally intensive.
A handful of people running these models on a personal computer within a limited time can provide a more accurate monsoon rainfall forecast compared to the resource intensive process involved with traditional physical models, the expert said.
