Unleashing the Power of Dynamic Mode Decomposition and Deep Learning for Rainfall Prediction in North-East India
This work addresses rainfall forecasting for disaster preparedness in a region prone to extreme weather, but it is incremental as it applies existing methods to a specific dataset.
The study tackled rainfall prediction in North-East India by comparing Dynamic Mode Decomposition (DMD) and Long Short-Term Memory (LSTM) models, finding that LSTM outperformed DMD in accuracy due to its ability to capture complex nonlinear relationships.
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.