AUTHOR=Chen Cai , Dong Jin TITLE=Deep learning approaches for time series prediction in climate resilience applications JOURNAL=Frontiers in Environmental Science VOLUME=Volume 13 - 2025 YEAR=2025 URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2025.1574981 DOI=10.3389/fenvs.2025.1574981 ISSN=2296-665X ABSTRACT=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.MethodsIn 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.ResultsExperimental evaluations on climate datasets demonstrate that our approach significantly improves forecasting accuracy, resilience indices, and equitable resource distribution compared to traditional models.DiscussionBy 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.