Data-driven predictive modelling and impact analysis of global temperature rise

Bole Zhang; Cong Wang; Fang Cheng; Lian He; Yuedan Zhang

doi:10.1117/12.3036557

21 July 2024 Data-driven predictive modelling and impact analysis of global temperature rise

Bole Zhang, Cong Wang, Fang Cheng, Lian He, Yuedan Zhang

Proceedings Volume 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024); 132193L (2024) https://doi.org/10.1117/12.3036557
Event: 4th International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2024), 2024, Kaifeng, China

Abstract

Based on data modeling and analytical techniques, this paper aims to establish a data-driven model for analyzing global temperature variations by examining the relationships between global temperatures, time, geographical locations, and regional disaster indices. The DBSCAN clustering method is employed to identify reference cities, and their temperature data are utilized to calculate the global average temperature. Future temperature projections are made using the Exponential Smoothing Time Series model and Grey Prediction Model. Subsequently, heatmaps and curves are drawn employing statistical data to elucidate the interplay between temperature, location, and time. This research provides objective and effective methods and measures for interpreting and responding to changes in global temperatures.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Bole Zhang, Cong Wang, Fang Cheng, Lian He, and Yuedan Zhang "Data-driven predictive modelling and impact analysis of global temperature rise", Proc. SPIE 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024), 132193L (21 July 2024); https://doi.org/10.1117/12.3036557

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