An Adaptive Stacking An Adaptive Stacking Approach for Monthly Rainfall Prediction with Hybrid Feature Selection

Rainfall is a critical climatic element for water resource management, agriculture, and hydrometeorological disaster mitigation. However, its nonlinear and fluctuating characteristics require a careful and adaptive predictive approach. This study aims to develop a monthly rainfall prediction model using an Adaptive Stacking Ensemble method combined with a hybrid feature selection framework. The feature selection integrates three techniques”correlation analysis, feature importance from Random Forest, and Recursive Feature Elimination (RFE)”through a voting mechanism. Three machine learning algorithms, namely Random Forest, K-Nearest Neighbors (KNN), and XGBoost, are used as base learners. The meta-learner is adaptively selected based on the best-performing base model. Model performance is evaluated using R², RMSE, and MAE metrics. The proposed method is expected to produce a more accurate, stable, and reliable predictive model to support climate-based decision-making. By leveraging the hybrid feature selection framework, the model effectively identifies the most relevant weather variables related to monthly rainfall patterns, thereby reducing model complexity without sacrificing accuracy. The adaptive stacking approach also offers flexibility in capturing nonlinear relationships between features and targets, while enhancing model generalization across seasonally varying data. Experiments were conducted on long-term weather datasets, and the results demonstrate that the proposed model outperforms single models and conventional ensemble methods. This research contributes to the development of more robust, data-driven climate prediction systems that can be applied across sectors affected by rainfall variability.