Accurate monthly forecasting of Rainfall pattern in Atlantic climates: an Empirical Fourier Decomposition-based Deep ensemble learning paradigm

The rainfall pattern plays a vital role in agriculture and overall climate resilience in the Atlantic provinces of Canada. Prince Edward Island and New Brunswick, two Atlantic provinces, have great potential to grow potatoes, grains, and blueberries. Thus, accurate forecasting of the rainfall pattern helps farmers determine the optimal time for planting, irrigation, fertilisation, and harvesting based on predicted rainfall patterns. Here, a new complementary multi-level intelligent framework comprised of the recursive feature elimination (RFE), Empirical Fourier Decomposition (EFD), and deep ensemble random vector functional link (Deep RVFL) has been developed to forecast the monthly (one month ahead) rainfall pattern in Charlottetown and Fredericton stations. Aiming for this, first, the significant antecedent information (lag sequences) was indicated using the RFE scheme. Then, all the optimal lags were decomposed using the EDF scheme to deduce the complexities of rainfall sub-component signals before feeding the Deep RVFL algorithm. Two comparative deep learning models, namely, RVFL and CNN-LSTM, were incorporated with the implemented multi-level pre-processing scheme in hybrid and standalone forms. In order to validate the models, several statistical indices, such as correlation coefficient (R), root mean square error (RMSE), and Kling-Gupta efficiency (KGE), scatter plots, signal trend analysis, and diagnostic assessment, were utilized. The outcomes of the results ascertained that the RFE-EDF-Deep RVFL framework, owing to superior forecasting performance, outperformed the RFE-EDF-Deep CNN-LSTM, RFE-EDF-RVFL and all the standalone counterpart models.