Predicting temperatures in Brazilian states capitals via Machine Learning
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915291386085376 |
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| author | da Silva, Sidney T. Gabrick, Enrique C. de Moraes, Ana Luiza R. Viana, Ricardo L. Batista, Antonio M. Caldas, Iberê L. Kurths, Jürgen |
| author_facet | da Silva, Sidney T. Gabrick, Enrique C. de Moraes, Ana Luiza R. Viana, Ricardo L. Batista, Antonio M. Caldas, Iberê L. Kurths, Jürgen |
| contents | Climate change refers to substantial long-term variations in weather patterns. In this work, we employ a Machine Learning (ML) technique, the Random Forest (RF) algorithm, to forecast the monthly average temperature for Brazilian's states capitals (27 cities) and the whole country, from January 1961 until December 2022. To forecast the temperature at $k$-month, we consider as features in RF: $i)$ global emissions of carbon dioxide (CO$_2$), methane (CH$_4$), and nitrous oxide (N$_2$O) at $k$-month; $ii)$ temperatures from the previous three months, i.e., $(k-1)$, $(k-2)$ and $(k-3)$-month; $iii)$ combination of $i$ and $ii$. By investigating breakpoints in the time series, we discover that 24 cities and the gases present breakpoints in the 80's and 90's. After the breakpoints, we find an increase in the temperature and the gas emission. Thereafter, we separate the cities according to their geographical position and employ the RF algorithm to forecast the temperature from 2010-08 until 2022-12. Based on $i$, $ii$, and $iii$, we find that the three inputs result in a very precise forecast, with a normalized root mean squared error (NMRSE) less than 0.083 for the considered cases. From our simulations, the better forecasted region is Northeast through $iii$ (NMRSE = 0.012). Furthermore, we also investigate the forecasting of anomalous temperature data by removing the annual component of each time series. In this case, the best forecasting is obtained with strategy $i$, with the best region being Northeast (NRMSE = 0.090). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_11511 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting temperatures in Brazilian states capitals via Machine Learning da Silva, Sidney T. Gabrick, Enrique C. de Moraes, Ana Luiza R. Viana, Ricardo L. Batista, Antonio M. Caldas, Iberê L. Kurths, Jürgen Atmospheric and Oceanic Physics Applications Climate change refers to substantial long-term variations in weather patterns. In this work, we employ a Machine Learning (ML) technique, the Random Forest (RF) algorithm, to forecast the monthly average temperature for Brazilian's states capitals (27 cities) and the whole country, from January 1961 until December 2022. To forecast the temperature at $k$-month, we consider as features in RF: $i)$ global emissions of carbon dioxide (CO$_2$), methane (CH$_4$), and nitrous oxide (N$_2$O) at $k$-month; $ii)$ temperatures from the previous three months, i.e., $(k-1)$, $(k-2)$ and $(k-3)$-month; $iii)$ combination of $i$ and $ii$. By investigating breakpoints in the time series, we discover that 24 cities and the gases present breakpoints in the 80's and 90's. After the breakpoints, we find an increase in the temperature and the gas emission. Thereafter, we separate the cities according to their geographical position and employ the RF algorithm to forecast the temperature from 2010-08 until 2022-12. Based on $i$, $ii$, and $iii$, we find that the three inputs result in a very precise forecast, with a normalized root mean squared error (NMRSE) less than 0.083 for the considered cases. From our simulations, the better forecasted region is Northeast through $iii$ (NMRSE = 0.012). Furthermore, we also investigate the forecasting of anomalous temperature data by removing the annual component of each time series. In this case, the best forecasting is obtained with strategy $i$, with the best region being Northeast (NRMSE = 0.090). |
| title | Predicting temperatures in Brazilian states capitals via Machine Learning |
| topic | Atmospheric and Oceanic Physics Applications |
| url | https://arxiv.org/abs/2505.11511 |