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Rainfall prediction using stacked deep learning networks
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DOI：10.1007/s40808-025-02523-8.png)
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Abstract
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Accurate rainfall prediction is a critical yet complex challenge in meteorological forecasting, essential for disaster preparedness, agricultural planning, and water resource management. Rainfall forecasting relies on the relationship between predictors and the rainfall being forecasted. In this study, a Stacked Bidirectional Long Short-Term Memory (SBiLSTM) network is proposed for rainfall time-series prediction. Leveraging bidirectional processing, the SBiLSTM model effectively captures complex temporal dependencies in both forward and backward directions, uncovering intricate relationships within the time series data. By stacking multiple BiLSTM layers, the model learns deeper and more complex patterns and relationships within the data, thereby enhancing the reliability and accuracy of rainfall forecasts. Meteorological data from Nigeria was used to train and evaluate the model. The methodology involves preprocessing the data and employing a cross-validation approach to train the SBiLSTM-based time series model. The performance of the SBiLSTM model was evaluated through simulations and compared against other deep learning architectures, including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), BiLSTM, and Multilayer Perceptron (MLP) models. The obtained results demonstrate that the SBiLSTM model achieved low mean absolute error (MAE) and root-mean-squared error (RMSE), along with a high R2 value for rainfall prediction of Nigeria, with scores of 4.4158, 9.962 and 0.987, respectively. For the local Kaduna region, the model achieved MAE, RMSE and R2 values of 51.831, 162.934, and 0.831, respectively. The results obtained indicate that the SBiLSTM model achieves superior predictive performance, highlighting its potential to improve the accuracy of rainfall forecasting in meteorological applications.
Keywords:
Rainfall prediction
Long short-term memory
SBiLSTM
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