Optimizing Drought Prediction with LSTM and SPEI: A Two-Tier Ensemble Framework with Meta-Learner and Weighted Sum Fusion

Abstract

Climate-induced water scarcity, especially in regions prone to gradual drought onset, poses a significant hurdle in effective water resource management. In this study, advanced data science techniques are harnessed, focusing on Latur as a region susceptible to prolonged dry spells. Latur, situated in the drought-prone Marathwada region, struggles with erratic rainfall and severe water stress, impacting both agricultural activities and daily necessities. Escalating temperatures intensifies water loss, heightening the risks of drought due to climate change. By leveraging time series data encompassing crucial environmental parameters such as rainfall and temperature, an improved model is developed for precise detection and visualization of droughts. Our approach combines Long Short-Term Memory (LSTM) layers with the Standardized Precipitation Evapotranspiration Index (SPEI), employing a multi-model ensemble framework that's further enhanced with meta-learning and weighted ensemble techniques. This innovative model not only showcases a notable enhancement in accuracy compared to conventional LSTM models but also exhibits adaptability and robustness across a wide range of datasets. The incorporation of SPEI serves to fine-tune the assessment of drought conditions. Selected data from the timeline spanning 1980 to 2022, with monthly timestamps, aligns with the specific characteristics of Latur's climate and serves as the basis for our approach. This project introduces a novel approach for drought forecasting, leveraging a Meta-Learning Ensemble model that synergistically combines various machine learning algorithms, including Random Forest, Gradient Boosting, and Neural Networks, to deliver highly accurate and actionable predictions. The ensemble approach not only capitalizes on the strengths of individual models but also significantly reduces the potential errors, offering a robust and reliable forecasting system. The system is particularly designed for researchers, policymakers, and farmers who need precise and timely information to make informed decisions. Predictive metrics are rigorously evaluated using statistical measures such as Mean Absolute Error and R-Squared, ensuring the highest level of accuracy. The model outputs are not just statistical metrics but actionable insights, allowing for pre-emptive measures to mitigate the adverse effects of drought. This advanced forecasting system serves as a groundbreaking contribution to climate science and natural disaster management. MSC Codes - 68T01, 68T07 JEL Codes - C32