Improvement in District Scale Heavy Rainfall Prediction Over Complex Terrain of North East India Using Deep Learning

Abstract

Predicting heavy rainfall events (HREs) in real time poses a significant challenge in India, particularly in complex terrain regions like Assam, where these hydro-meteorological events frequently associated with flash floods with severe consequences over region. The devastating HREs in June 2022 led to numerous casualties, extensive damage, and economic losses exceeding 200 crore, necessitating the evacuation of over 4 million individuals. As we write this paper Assam again going through immense flooding situation in now i.e. June2023. Due to the limitations of deterministic numerical weather models in accurately forecasting these events, the study explores the incorporation of deep learning (DL) models, specifically U-Nets, using simulated daily accumulated rainfall outputs from various parametrization schemes. Over a four-day period in June 2022, the U-Net based model demonstrated superior skills in predicting rainfall at the district scale, achieving a Mean Absolute Error (MAE) of less than 12mm, outperforming individual and ensemble model outputs. Comparing the DL model’s performance to the Weather Research and Forecasting (WRF) forecasts, it exhibited a remarkable 64.78% reduction in MAE across Assam. Notably, the proposed model accurately predicted HREs in specific districts such as Barpeta, Kamrup, Kokrajhar, and Nalbari, showcasing improved spatial variation compared to the WRF model. The DL model’s predictions aligned with actual rainfall (> 150 mm) observations from the India Meteorological Department (IMD), while the WRF forecasts consistently underestimated rainfall intensity (< 100 mm). Furthermore, the proposed model achieved a high prediction accuracy of 77.9% in categorical rainfall prediction, significantly outperforming the WRF schemes by 38.1%.