Unleashing the Power of Dynamic Mode Decomposition and Deep Learning for
  Rainfall Prediction in North-East India
Paleti Nikhil Chowdary Sathvika P Pranav U Rohan S Sowmya V Gopalakrishnan E A Dhanya M
Accurate rainfall forecasting is crucial for effective disaster preparedness
and mitigation in the North-East region of India, which is prone to extreme
weather events such as floods and landslides. In this study, we investigated
the use of two data-driven methods, Dynamic Mode Decomposition (DMD) and Long
Short-Term Memory (LSTM), for rainfall forecasting using daily rainfall data
collected from India Meteorological Department in northeast region over a
period of 118 years. We conducted a comparative analysis of these methods to
determine their relative effectiveness in predicting rainfall patterns. Using
historical rainfall data from multiple weather stations, we trained and
validated our models to forecast future rainfall patterns. Our results indicate
that both DMD and LSTM are effective in forecasting rainfall, with LSTM
outperforming DMD in terms of accuracy, revealing that LSTM has the ability to
capture complex nonlinear relationships in the data, making it a powerful tool
for rainfall forecasting. Our findings suggest that data-driven methods such as
DMD and deep learning approaches like LSTM can significantly improve rainfall
forecasting accuracy in the North-East region of India, helping to mitigate the
impact of extreme weather events and enhance the region's resilience to climate
change.