Bhubaneswar: IIT-Bhubaneswar has developed a hybrid technology to enhance rainfall prediction accuracy, particularly in case of downpours with an adequate lead time, by integrating the output from the Weather Research and Forecasting (WRF) model into a deep learning (DL) model.
“The studies were carried out using retrospective cases over the complex terrain of Assam (highly vulnerable to severe flooding) during June 2023 and over the state of Odisha where heavy rainfall events are highly dynamic in nature due to the landfall of multiple intense rain bearing monsoon low-pressure systems,” the institute said in a statement on Monday.
It claimed that the hybrid model displayed prediction accuracy, nearly double that of traditional ensemble models, at a district level in Assam with a lead time up to 96 hours.
Between June 13 and 17, 2023, Assam experienced severe flooding due to heavy rainfall.
“The DL model was able to more accurately predict the spatial distribution and intensity of rainfall across at districts scale. The research employed the WRF model to generate initial weather forecasts in real time, which were then refined using the DL model. This method allowed for a more detailed analysis of rainfall patterns, incorporating a spatio-attention module to better capture the intricate spatial dependencies in the data,” it said.
The study titled ‘Minimization of Forecast Error Using Deep Learning for Real-Time Heavy Rainfall Events Over Assam’, published in IEEE Xplore, has revealed that integrating DL with the traditional WRF model dramatically improves forecast accuracy for heavy rainfall events in real-time, a critical advancement for this flood-prone mountainous region like Assam, it added.
