Time Series Forecasting and Soft Sensing for Climate Modeling and Applications
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".
Deadline for manuscript submissions: 20 April 2026 | Viewed by 92
Special Issue Editors
Interests: machine vision; robotics; medical AI
Special Issues, Collections and Topics in MDPI journals
Interests: deep learning; computer vision; digital twinning; sensor technologies
Special Issues, Collections and Topics in MDPI journals
Interests: deep learning; time series prediction; soft sensing; control
Special Issue Information
Dear Colleagues,
With the development of artificial intelligence, data-driven modeling, and advanced computational techniques, time series forecasting and soft sensing have become essential tools for understanding and predicting climate variability. Climate systems are inherently complex, nonlinear, and influenced by multiple spatiotemporal factors, which makes accurate modeling and forecasting highly challenging.
This Special Issue aims to bring together recent advances in methodologies, algorithms, and applications of time series forecasting and soft sensing techniques in the context of climate modeling. Topics of interest include, but are not limited to, the following:
- Machine learning and deep learning approaches for climate time series prediction
- Hybrid models combining physical and data-driven methods
- Soft sensing methods for estimating unmeasured or hard-to-measure climate variables
- Uncertainty quantification and interpretability in climate forecasting models
- Applications of time series forecasting in renewable energy, hydrology, and atmospheric sciences
- Case studies on real-world climate data and large-scale simulations
- Edge and cloud computing for climate data analysis and sensing
This issue seeks contributions that address theoretical foundations, methodological innovations, and practical applications to advance state-of-the-art in climate modeling and its societal and environmental impacts.
Dr. Ziyang Wang
Prof. Dr. Yifan Zhao
Guest Editors
Dr. Yongxiang Lei
Guest Editor Assistant
Manuscript Submission Information
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Keywords
- time series prediction
- deep learning
- climate prediction
- embodied intelligence-based applications
- soft sensing
- explainable AI
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