Abstract
Climate is the result of the constant interaction between different weather variables where temperature and precipitation are significant factors. Precipitation refers to the condensation of water vapor from clouds as a result of gravitational pull. These variables act as governing factors for determining rainfall, snowfall, and air pressure while determining wide-ranging effects on ecosystems. Different calculation methods could be employed such as Standard Precipitation Index for determining precipitation. Temperature is the measure that is used to identify the heat energy generated by solar radiation and other industrial factors. For understanding the interplay between these two variables, data gathered from several regions of the world including North America, Europe, Australia, and Central Asia was analyzed, and the findings are presented in this paper. Prediction methods such as multiple linear regression and long short-term memory (LSTM) have been employed for predicting rainfall from temperature and precipitation data. The inter-dependency of other weather parameters is also observed in this paper relating to rainfall prediction. The accuracy of the prediction models using machine learning has also been experimented within the study. The implementation of our work is available via https://github.com/MadaraPremawardhana/On-the-Impact-of-Temperature-for-Precipitation-Analysis.
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Notes
- 1.
The source code related to this paper is available via https://github.com/MadaraPremawardhana/On-the-Impact-of-Temperature-for-Precipitation-Analysis.
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Acknowledgements
This research was conducted with the financial support of Science Foundation Ireland under Grant Agreement No. 13/RC/2106_P2 at the ADAPT SFI Research Centre at University College Dublin. ADAPT, the SFI Research Centre for AI-Driven Digital Content Technology is funded by Science Foundation Ireland through the SFI Research Centres Programme.
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Premawardhana, M., Azeem, M.A., Sengar, S.S., Dev, S. (2023). On the Impact of Temperature for Precipitation Analysis. In: Bhateja, V., Yang, XS., Ferreira, M.C., Sengar, S.S., Travieso-Gonzalez, C.M. (eds) Evolution in Computational Intelligence. FICTA 2023. Smart Innovation, Systems and Technologies, vol 370. Springer, Singapore. https://doi.org/10.1007/978-981-99-6702-5_14
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