Rainfall plays a crucial role in shaping agriculture, water resource planning, and disaster management, especially in countries like India where millions of livelihoods depend on the seasonal monsoon. Accurate rainfall forecasting is therefore of great importance, yet traditional statistical models often struggle to capture the highly non-linear, uncertain, and time-dependent nature of rainfall patterns. With the advancement of artificial intelligence, deep learning techniques such as Recurrent Neural Networks (RNNs) have emerged as promising alternatives for handling sequential climate data. In this project, five RNN-based models—Simple RNN, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM, and Stacked LSTM—were implemented and evaluated on India’s historical rainfall dataset covering the years 1901 to 2015. The dataset was preprocessed through normalization and transformed into time-series sequences for effective learning. Each model was trained and compared using regression metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), along with confusion matrices to classify rainfall levels into categories. The experimental findings reveal that advanced architectures like Bidirectional LSTM and Stacked LSTM achieved superior accuracy compared to the baseline Simple RNN. These results highlight the strong potential of deep learning methods in improving rainfall forecasting and support their application in climate modeling and decision-making for agriculture and disaster preparedness.
Rainfall Forecasting, Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Time-Series Prediction, Deep Learning, Climate Modeling, Predictive Analysis, Monsoon Predictive.
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