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.

使用 Dynamic Mode Decomposition (DMD) 和 Long Short-Term Memory (LSTM) 这两种数据驱动的方法，研究了印度东北地区 118 年来的日降雨数据，并进行了降雨预测的相对效果比较分析，结果显示 LSTM 在准确性方面优于 DMD，展现了 LSTM 捕捉数据中复杂非线性关系的能力，从而成为降雨预测的强大工具，并提出数据驱动方法和深度学习方法可以显著提高印度东北地区的降雨预测准确性，帮助减轻极端天气事件的影响并增强该地区对气候变化的适应能力。