A Fundamental Framework for Analysis of Rainfall Prediction Features Significance

Rainfall prediction efforts had been prevalent ever since the impact of climate change on occurrences of natural disasters globally. Implementation of machine and deep learning techniques on features that contribute to rainfall occurrences were conducted with aims of seeking greater prediction accuracy for rainfall occurrences with a lack of study for significance of features in rainfall occurrence prediction. This study presents a framework of rainfall prediction features' significance analysis in the case study of Peninsular Malaysia rainfall occurrences. Features investigated in this study consist of temperature, humidity and wind speed. The designed framework for the investigation includes phases of data collection, data preprocessing, integration of random forest (RF) for ensemble classification and feature importance (FI) for feature significance calculation and finally model evaluation based on the metrics of precision, recall, F1 score and receiver operating characteristic (ROC) curve. In the preliminary investigation, the prediction model demonstrated accuracy, precision, recall and F1-score of 80.65%, 80%, 81% and 0.80 respectively. Humidity was found to have highest significance to the model's predictive power as compared to temperature and wind speed. Rainfall occurrence correlation with lower temperature and higher humidity and vice versa was identified with further investigation of feature data distribution against rainfall occurrences.