Deep learning approaches for time series prediction in climate resilience applications

Introduction Time series prediction is a fundamental task in climate resilience, where accurate forecasting of climate variables is critical for proactive planning and adaptation. Traditional methods often struggle with the nonlinearity, high variability, and multi-scale dependencies inherent in climate data, limiting their applicability in dynamic and diverse environments. Methods In this work, we propose a novel framework that combines the Resilience Optimization Network (ResOptNet) with the Equity-Driven Climate Adaptation Strategy (ED-CAS) to address these challenges. ResOptNet employs hybrid predictive modeling and multi-objective optimization to identify tailored interventions for climate risk mitigation, dynamically adapting to real-time data through a feedback-driven loop. ED-CAS complements this by embedding equity considerations into resource allocation, ensuring that resilience-building efforts prioritize vulnerable populations and regions. Results Experimental evaluations on climate datasets demonstrate that our approach significantly improves forecasting accuracy, resilience indices, and equitable resource distribution compared to traditional models. Discussion By integrating predictive analytics with optimization and equity-driven strategies, this framework provides actionable insights for climate adaptation, advancing the development of scalable and socially just resilience solutions.