The study was conducted in complex terrain of Assam.

Bhubaneswar: The Indian Institute of Technology (IIT)-Bhubaneswar has developed a hybrid technology that integrates the output from a Weather Research and Forecasting (WRF) model into a deep learning (DL) model, specifically with the aim of improving forecasting of extreme rainfall and increasing forecast accuracy. program with sufficient lead time, official sources said on Monday.

The study also highlighted the potential of artificial intelligence to improve real-time weather forecasting, especially for extreme rainfall events in the complex terrain of the Indian region.

The study was conducted in June 2023 over the complex terrain of Assam (prone to severe flooding) and Odisha state, where extreme rainfall events are highly dynamic in nature due to the terrain of multiple heavy rainfall monsoon low-pressure systems. .

“In Assam, the hybrid model shows forecast accuracy that is almost double that of conventional models with a lead time of up to 96 hours at the district level, indicating its remarkable performance. These innovative studies have been conducted using retrospective cases,” official sources said.

Researchers at IIT-Bhubaneswar have made a significant leap in accurate real-time forecasting of extreme rainfall events in the region using deep learning techniques in another landmark study. The study demonstrated the robustness of the new hybrid technology for real time conditions over the complex terrain of Assam.

A study titled 'Minimization of Forecast Error Using Deep Learning for Real-time Heavy Rainfall Events Over Assam' published in IEEE Xplore shows that combining DL with conventional WRF models dramatically improves forecast accuracy for real-time heavy rainfall events. , a significant breakthrough for this flood-prone hilly region like Assam,” the sources added.

Between 13 and 17 June 2023, heavy rains caused severe flooding in Assam. The DL model was able to more accurately predict the spatial distribution and intensity of rainfall at the district level. The research used the WRF model to produce initial weather forecasts in real time, which were then refined using the DL model.

With this new method, experts can now analyze rainfall patterns in more detail, incorporating a spatio-attention module to better capture complex spatial dependencies in the data.

As discussed, the model was trained using data from past extreme rainfall events, to improve its accuracy, from the output of several components as well as observations from the India Meteorological Department (IMD).

“This progress is critical to reducing the impact of natural disasters and public safety. Apart from this, these pioneering works will serve as a guiding light for creating similar hybrid models for other complex topographical terrains like the Western Himalayas and Western Ghats regions of India,” official sources said.