A hybrid univariate data preprocessing using overlapping flexible sliding window and DWT for rainfall prediction using deep learning ensemble techniques

Accurately predicting rainfall is still a difficult but crucial task in meteorological forecasting, with significant implications for agriculture, disaster planning, and water resource management. In the past, statistical methods were used to forecast rainfall, however these methods were less accurate due to data dynamics and other problems. To address the data issue in this study, Discrete Wavelet Transform (DWT) was employed, it improves rainfall data preprocessing by decomposing data, eliminating noise, risk of overfitting and concentrating on significant patterns. Overlapping Flexible Sliding Window (OFSW) technique is essential since it dynamically modifies window sizes according to data properties unlike the overlapping sliding windows. Leveraging the advancements in Deep Learning (DL), this paper presents rainfall prediction by employing DL algorithms Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and Multilayer Perceptron (MLPs). Furthermore, the standalone models were fused to form a weighted average ensemble. The monthly data from Nigeria and the Kaduna region were gathered and used in forecasting. The Bi-LSTM model has shown better performance compared to other models in predicting rainfall univariate time series data. In this study, these models are integrated into an ensemble structure for prediction purposes. The obtained ensemble learning model harmonizes the advantages of CNNs, Bi-LSTMs, and MLPs. The results of the weighted average ensemble demonstrated the benefits of the ensemble model in rainfall prediction in Kaduna by achieving the least MSE of 0.0018,0.04242, 0.0303 of MSE, RMSE and MAE in the training phase respectively, while in the testing phase, it achieves, 0.0041, 0.0640 and 0.0447 of MSE, RMSE and MAE in the testing phase respectively. MSE, RMSE and MAE of 0.0017, 0.0412, and 0.0301 for Nigeria in the training phase respectively, while in the testing phase, 0.0042, 0.0648 and 0.0457 of MSE, RMSE and MAE were achieved.