Study and Prediction of Extreme Rainfall Events On Indian Region

Abstract

Extreme rainfall events, commonly known as cloudbursts, are significant weather phenomena characterized by exceptionally high intensity of precipitation within a short period. These events can result in devastating flash floods, landslides, and avalanches, causing extensive damage to infrastructure, property, and significant loss of life. Despite various measures and advancements in meteorological science to mitigate their impact, predicting these events with high accuracy remains a formidable challenge. This study focuses on applying cutting-edge machine learning techniques to anticipate heavy rainfall events in the Indian subcontinent, specifically leveraging ConvLSTM neural networks. By integrating diverse meteorological datasets, including potential vorticity, relative humidity, cloud cover, temperature, and surface pressure, this research aims to develop a robust predictive model. Leveraging the historical data, the ConvLSTM model is trained to discern intricate patterns and correlations between the input variables and cloudburst incidence, thus enabling accurate predictions of cloudburst probabilities within future timeframes. The empirical findings of this study reveal the ConvLSTM-based prediction model’s remarkable accuracy and its capacity to furnish valuable insights into cloudburst event occurrences. To summarize, this project encompasses the development of an advanced ConvLSTM-based prediction model for cloudburst events, effectively harnessing historical meteorological data