A Hybrid Ensemble Learning Approach for Accurate SPI₆-Based Drought Forecasting in Semi-Arid Regions

Abstract

This study proposes a hybrid machine learning framework to predict the six-month Standardized Precipitation Index (SPI₆) for meteorological drought assessment in Nanded, India, using NASA POWER data (1994–2024). Four models Ridge Regression, Random Forest, Multi-Layer Perceptron (MLP) and a stacking ensemble (Ensemble_StackLR) were developed and evaluated using R², RMSE, MAE, NSE and PBIAS. Random Forest and MLP showed strong predictive capability, while Ridge Regression provided stable but comparatively lower performance in capturing nonlinear patterns. The Ensemble_StackLR model delivered the most robust and balanced results, achieving R² between 0.87 and 0.91, high correlation (r ≈ 0.96) and minimal bias across training, validation and testing datasets. It effectively captured drought onset, duration and recovery phases, outperforming individual models in stability and generalization. The framework demonstrates that ensemble learning enhances SPI prediction accuracy and offers a scalable, data-driven solution for drought monitoring and water resource management in data-scarce regions.