Version 1
: Received: 13 May 2024 / Approved: 14 May 2024 / Online: 14 May 2024 (06:50:01 CEST)
How to cite:
AlBalawi, E. Exploration of Climate Data and Temperature Forecasting using Machine Learning. Preprints2024, 2024050910. https://doi.org/10.20944/preprints202405.0910.v1
AlBalawi, E. Exploration of Climate Data and Temperature Forecasting using Machine Learning. Preprints 2024, 2024050910. https://doi.org/10.20944/preprints202405.0910.v1
AlBalawi, E. Exploration of Climate Data and Temperature Forecasting using Machine Learning. Preprints2024, 2024050910. https://doi.org/10.20944/preprints202405.0910.v1
APA Style
AlBalawi, E. (2024). Exploration of Climate Data and Temperature Forecasting using Machine Learning. Preprints. https://doi.org/10.20944/preprints202405.0910.v1
Chicago/Turabian Style
AlBalawi, E. 2024 "Exploration of Climate Data and Temperature Forecasting using Machine Learning" Preprints. https://doi.org/10.20944/preprints202405.0910.v1
Abstract
In this short communication, a concept has been presented to model geographical data to predict future temperature of Tabuk, region. Machine learning has been applied to the weather station data to develop a prediction model. The preliminary results are promising and encouraging and are envisaging to further this research towards the determination of unknown temperature rise in the region. This is important to mention here, that the problem has been formulated as a Regression problem, NOT as a classification problem. Hence, applying Convolutional neural networks is not possible, due to the non-existence of classes or converting the temperature values to classes does not make any sense. Hence, this is defined as a regression problem which achieved encouraging desirable results.
Keywords
machine learning; geographical data; temperature prediction
Subject
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.