Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 7, 09 October 2023
Open
Access | Article
Extensive analysis of rain in australia by exploratory data analysis, feature engineering and modeling
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Abstract
Accurate rainfall forecasts help in planning outdoor activities, agricultural practices, and water resource management, thereby mitigating the impact of extreme weather events. This article provides an in-depth analysis of rainfall in Australia, focusing on predicting whether it will rain tomorrow using logistic regression. The research aims to develop an accurate model to help predict rainfall events for better preparedness and planning. We obtained datasets from a number of Australian weather stations. The dataset contains 142,193 daily weather observations spanning approximately ten years. The recorded information includes various details such as date, location, humidity, wind direction, clouds, temperature, etc. This shows that the model performs well in distinguishing between rainy and non-rainy days with an accuracy of about 0.875. The findings of this study have important implications for various stakeholders including meteorologists, disaster management agencies, and the public.
Keywords
logistic regression, weather forecasting, data analysis, feature engineering, machine learning
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2023 International Conference on Environmental Geoscience and Earth Ecology
- ISBN (Print)
- 978-1-83558-015-8
- ISBN (Online)
- 978-1-83558-016-5
- Published Date
- 09 October 2023
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/7/20230115
- Copyright
- 09 October 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited