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Accurate rainfall forecasting is crucial for effective disaster preparednessand mitigation in the North-East region of India, which is prone to extremeweather events such as floods and landslides. In this study, we investigatedthe use of two data-driven methods, Dynamic Mode Decomposition (DMD) and LongShort-Term Memory (LSTM), for rainfall forecasting using daily rainfall datacollected from India Meteorological Department in northeast region over aperiod of 118 years. We conducted a comparative analysis of these methods todetermine their relative effectiveness in predicting rainfall patterns. Usinghistorical rainfall data from multiple weather stations, we trained andvalidated our models to forecast future rainfall patterns. Our results indicatethat both DMD and LSTM are effective in forecasting rainfall, with LSTMoutperforming DMD in terms of accuracy, revealing that LSTM has the ability tocapture complex nonlinear relationships in the data, making it a powerful toolfor rainfall forecasting. Our findings suggest that data-driven methods such asDMD and deep learning approaches like LSTM can significantly improve rainfallforecasting accuracy in the North-East region of India, helping to mitigate theimpact of extreme weather events and enhance the region's resilience to climatechange.

Unleashing the Power of Dynamic Mode Decomposition and Deep Learning for Rainfall Prediction in North-East India