Publication
Abstract
Temperature serves as a fundamental indicator of thermal conditions, influencing various natural processes and human activities. This study investigates the relationship between temperature and other meteorological factors, including humidity, wind speed, visibility, pressure, and apparent temperature, using historical weather data from Szeged, Hungary (2006-2016). Employing multiple regression models and advanced machine learning algorithms such as XGBoost and Artificial Neural Networks (ANNs), the research aims to elucidate the linear and non-linear dependencies of temperature on these factors. The findings indicate a significant linear correlation, with XGBoost outperforming traditional regression approaches in predicting temperature variations. This study contributes to enhancing temperature forecasting accuracy, which is crucial for improving quality of life and informing climate-related decision-making processes.
Keywords
Machine Learning , Artificial Neural Networks , Regression Model .
Metadata
Disciplines: Computer Science
Subjects: Machine Learning
Cite This Article
APA Style
Che, C. & Tian, J. (2024). Understanding the interrelation between temperature and meteorological factors: a case study of szeged using machine learning techniques. Journal of Computer Technology and Applied Mathematics, 1(4), 47-52. https://doi.org/10.5281/zenodo.13924235
Acknowledgments
The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.
FUNDING
INSTITUTIONAL REVIEW BOARD STATEMENT
DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
INFORMED CONSENT STATEMENT
CONFLICT OF INTEREST
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
AUTHOR CONTRIBUTIONS
References
1.
Cheng, X., & Lin, J. (2024). Is electric truck a viable alternative to diesel truck in long-haul operation? Transportation Research Part D: Transport and Environment, 129, 104119.
2.
Cheng, X., Nie, Y. M., & Lin, J. (2024). An autonomous modular public transit service. Transportation Research Part C: Emerging Technologies, 104746.
3.
Ghadban, M., Baayoun, A., Lakkis, I., Najem, S., Saliba, N., & Shihadeh, A. (2020). A novel method to improve temperature forecast in data-scarce urban environments with application to the Urban Heat Island in Beirut. Urban Climate, 33, 100648. https://doi.org/10.1016/j.uclim.2020.100648
4.
Guo, G., Li, X., Zhu, C., Wu, Y., Chen, J., Chen, P., & Cheng, X. (2025). Establishing benchmarks to determine the embodied carbon performance of high-speed rail systems. Renewable and Sustainable Energy Reviews, 207, 114924. https://doi.org/10.1016/j.rser.2021.114924
5.
Kuo, Y. H., Leung, J. M., & Yan, Y. (2023). Public transport for smart cities: Recent innovations and future challenges. European Journal of Operational Research, 306(3), 1001-1026. https://doi.org/10.1016/j.ejor.2023.01.023
6.
Liu, K., Ding, K., Cheng, X., Chen, J., Feng, S., Lin, H., ... & Zhu, C. (2024). Airport Delay Prediction with Temporal Fusion Transformers. arXiv preprint arXiv:2405.08293.
7.
Liu, T., & Meidani, H. (2024). End-to-end heterogeneous graph neural networks for traffic assignment. Transportation Research Part C: Emerging Technologies, 165, 104695.
8.
Liu, T., & Meidani, H. (2024). Graph Neural Network Surrogate for Seismic Reliability Analysis of Highway Bridge Systems. Journal of Infrastructure Systems, 30(4), 05024004.
9.
Liu, T., & Meidani, H. (2024). Heterogeneous Graph Sequence Neural Networks for Dynamic Traffic Assignment. arXiv preprint arXiv:2408.04131.
10.
Liu, T., & Meidani, H. (2024). Neural network surrogate models for aerodynamic analysis in truck platoons: Implications on autonomous freight delivery. International Journal of Transportation Science and Technology (2024).
11.
Mateo, F., Carrasco, J., Sellami, A., Millán-Giraldo, M., Domínguez, M., & Soria-Olivas, E. (2013). Machine learning methods to forecast temperature in buildings. Expert Systems with Applications, 40(4), 1061-1068. https://doi.org/10.1016/j.eswa.2012.09.030
12.
Su, G., Cheng, X., Feng, S., Liu, K., Song, J., Chen, J., ... & Lin, H. (2024). Flight Path Optimization with Optimal Control Method. arXiv preprint arXiv:2405.08306.
13.
Ying, C., Chow, A. H., Yan, Y., Kuo, Y. H., & Wang, S. (2024). Adaptive rescheduling of rail transit services with short-turnings under disruptions via a multi-agent deep reinforcement learning approach. Transportation Research Part B: Methodological, 188, 103067.
14.
Yan, Y., Chow, A. H., Ho, C. P., Kuo, Y. H., Wu, Q., & Ying, C. (2022). Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities. Transportation Research Part E: Logistics and Transportation Review, 162, 102712. https://doi.org/10.1016/j.tre.2022.102712
15.
Yan, Y., Deng, Y., Cui, S., Kuo, Y. H., Chow, A. H., & Ying, C. (2023). A policy gradient approach to solving dynamic assignment problem for on-site service delivery. Transportation Research Part E: Logistics and Transportation Review, 178, 103260. https://doi.org/10.1016/j.tre.2023.103260
16.
Yan, Y., Wen, H., Deng, Y., Chow, A. H., Wu, Q., & Kuo, Y. H. (2024). A mixed-integer programming-based Q-learning approach for electric bus scheduling with multiple termini and service routes. Transportation Research Part C: Emerging Technologies, 162, 104570. https://doi.org/10.1016/j.trc.2023.104570
17.
Yan, Y., Cui, S., Liu, J., Zhao, Y., Zhou, B., & Kuo, Y. H. (2024). Multimodal fusion for large-scale traffic prediction with heterogeneous retentive networks. Information Fusion, 102695. https://doi.org/10.1016/j.inffus.2023.102695
18.
Yi, C., Shin, Y., & Roh, J. (2018). Development of an Urban High-Resolution Air Temperature Forecast System for Local Weather Information Services Based on Statistical Downscaling. Atmosphere, 9(5), 164. https://doi.org/10.3390/atmos9050164
19.
Zeynoddin, M., Bonakdari, H., Ebtehaj, I., Esmaeilbeiki, F., Gharabaghi, B., & Zare Haghi, D. (2019). A reliable linear stochastic daily soil temperature forecast model. Soil and Tillage Research, 189, 73-87. https://doi.org/10.1016/j.still.2019.02.001
20.
Cheng, X., Shen, H., Huang, Y., Cheng, Y. L., & Lin, J. (2024, February). Using Mobile Charging Drones to Mitigate Battery Disruptions of Electric Vehicles on Highways. In 2024 Forum for Innovative Sustainable Transportation Systems (FISTS) (pp. 1-6). IEEE.
21.
Cheng, X., Mamalis, T., Bose, S., & Varshney, L. R. (2024). On Carsharing Platforms With Electric Vehicles as Energy Service Providers. IEEE Transactions on Intelligent Transportation Systems.
22.
Che, C., Li, C., & Huang, Z. (2024). The Integration of Generative Artificial Intelligence and Computer Vision in Industrial Robotic Arms. International Journal of Computer Science and Information Technology, 2(3), 1-9.
23.
Tianbo, S., Weijun, H., Jiangfeng, C., Weijia, L., Quan, Y., & Kun, H. (2023, January). Bio-inspired swarm intelligence: a flocking project with group object recognition. In 2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE) (pp. 834-837). IEEE.
24.
Xu, J., Jiang, Y., Yuan, B., Li, S., & Song, T. (2023, November). Automated Scoring of Clinical Patient Notes using Advanced NLP and Pseudo Labeling. In 2023 5th International Conference on Artificial Intelligence and Computer Applications (ICAICA) (pp. 384-388). IEEE.
25.
Zhang, Q., Cai, G., Cai, M., Qian, J., & Song, T. (2023). Deep Learning Model Aids Breast Cancer Detection. Frontiers in Computing and Intelligent Systems, 6(1), 99-102.
26.
Xu, X., Yuan, B., Song, T., & Li, S. (2023, November). Curriculum recommendations using transformer base model with infonce loss and language switching method. In 2023 5th International Conference on Artificial Intelligence and Computer Applications (ICAICA) (pp. 389-393). IEEE.
27.
Yuan, B., & Song, T. (2023, November). Structural Resilience and Connectivity of the IPv6 Internet: An AS-level Topology Examination. In Proceedings of the 4th International Conference on Artificial Intelligence and Computer Engineering (pp. 853-856).
28.
Yuan, B., Song, T., & Yao, J. (2024, January). Identification of important nodes in the information propagation network based on the artificial intelligence method. In 2024 4th International Conference on Consumer Electronics and Computer Engineering (ICCECE) (pp. 11-14). IEEE.
29.
Qian, J., Song, T., Zhang, Q., Cai, G., & Cai, M. (2023). Analysis and Diagnosis of Hemolytic Specimens by AU5800 Biochemical Analyzer Combined With AI Technology. Frontiers in Computing and Intelligent Systems, 6(3), 100-103.
30.
Cai, G., Qian, J., Song, T., Zhang, Q., & Liu, B. (2023). A deep learning-based algorithm for crop Disease identification positioning using computer vision. International Journal of Computer Science and Information Technology, 1(1), 85-92.
31.
Song, T., Zhang, Q., Cai, G., Cai, M., & Qian, J. (2023). Development of Machine Learning and Artificial Intelligence in Toxic Pathology. Frontiers in Computing and Intelligent Systems, 6(3), 137-141.
32.
Liu, B., Cai, G., Qian, J., Song, T., & Zhang, Q. (2023). Machine Learning Model Training and Practice: A Study on Constructing a Novel Drug Detection System. International Journal of Computer Science and Information Technology, 1(1), 139-146.
33.
Che, C., Lin, Q., Zhao, X., Huang, J., & Yu, L. (2023, September). Enhancing Multimodal Understanding with CLIP-Based Image-to-Text Transformation. In Proceedings of the 2023 6th International Conference on Big Data Technologies (pp. 414-418).
34.
Che, C., Hu, H., Zhao, X., Li, S., & Lin, Q. (2023). Advancing Cancer Document Classification with R andom Forest. Academic Journal of Science and Technology, 8(1), 278-280.
35.
Huang, Z., Zheng, H., Li, C., & Che, C. (2024). Application of machine learning-based k-means clustering for financial fraud detection. Academic Journal of Science and Technology, 10(1), 33-39.
36.
Huang, Z., Che, C., Zheng, H., & Li, C. (2024). Research on Generative Artificial Intelligence for Virtual Financial Robo-Advisor. Academic Journal of Science and Technology, 10(1), 74-80.
37.
Che, C., Huang, Z., Li, C., Zheng, H., & Tian, X. (2024). Integrating generative AI into financial market prediction for improved decision making. Applied and Computational Engineering, 64, 155-161.
38.
Liu, H., Wang, C., Zhan, X., Zheng, H., & Che, C. (2024). Enhancing 3D Object Detection by Using Neural Network with Self-adaptive Thresholding. arXiv preprint arXiv:2405.07479.
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