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 cli...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Environmental Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenvs.2025.1574981/full |
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| author | Cai Chen Jin Dong |
| author_facet | Cai Chen Jin Dong |
| author_sort | Cai Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-29a159edf79f4a0987e846c8eb8a8aa0 |
| institution | OA Journals |
| issn | 2296-665X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Environmental Science |
| spelling | doaj-art-29a159edf79f4a0987e846c8eb8a8aa02025-08-20T02:29:50ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2025-04-011310.3389/fenvs.2025.15749811574981Deep learning approaches for time series prediction in climate resilience applicationsCai Chen0Jin Dong1Sichuan Digital Economy Industry Development Research Institute, Xi‘an Jiaotong University, Chengdu, ChinaDepartment of Art and Design, Taiyuan University, Taiyuan, Shanxi, ChinaIntroduction 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.https://www.frontiersin.org/articles/10.3389/fenvs.2025.1574981/fulltime series predictionclimate resilienceequity-driven adaptationmulti-objective optimizationreal-time feedback |
| spellingShingle | Cai Chen Jin Dong Deep learning approaches for time series prediction in climate resilience applications Frontiers in Environmental Science time series prediction climate resilience equity-driven adaptation multi-objective optimization real-time feedback |
| title | Deep learning approaches for time series prediction in climate resilience applications |
| title_full | Deep learning approaches for time series prediction in climate resilience applications |
| title_fullStr | Deep learning approaches for time series prediction in climate resilience applications |
| title_full_unstemmed | Deep learning approaches for time series prediction in climate resilience applications |
| title_short | Deep learning approaches for time series prediction in climate resilience applications |
| title_sort | deep learning approaches for time series prediction in climate resilience applications |
| topic | time series prediction climate resilience equity-driven adaptation multi-objective optimization real-time feedback |
| url | https://www.frontiersin.org/articles/10.3389/fenvs.2025.1574981/full |
| work_keys_str_mv | AT caichen deeplearningapproachesfortimeseriespredictioninclimateresilienceapplications AT jindong deeplearningapproachesfortimeseriespredictioninclimateresilienceapplications |