A Study on the performance of Four Regression Models in Predicting Weather Temperature Based on Python
For industries like agriculture and disaster management, weather forecasting is essential. This study assesses how well four regression models—linear regression, random forest regression, support vector regression (SVR), and K-Nearest Neighbors (KNN)—predict weather temperatures using a dataset from...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02017.pdf |
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| author | Li Taobei |
| author_facet | Li Taobei |
| author_sort | Li Taobei |
| collection | DOAJ |
| description | For industries like agriculture and disaster management, weather forecasting is essential. This study assesses how well four regression models—linear regression, random forest regression, support vector regression (SVR), and K-Nearest Neighbors (KNN)—predict weather temperatures using a dataset from England. Standardizing and expanding features were part of the data preprocessing process to capture non-linear interactions. Performance metrics were used to evaluate the models' predictive capacity. With the highest R2 value and the lowest error metrics, Random Forest Regression fared better than the other models, suggesting higher predictive accuracy, according to the data. KNN exhibited greater sensitivity to local fluctuations compared to SVR, which performed slightly better overall. linear Regression was the least effective, struggling with non-linear data and exhibiting higher error metrics. This study offers a thorough comparison of weather prediction regression models, emphasizing the performance of the Random Forest regression. |
| format | Article |
| id | doaj-art-c2bae40c04804f24a0871efc28ba52ba |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-c2bae40c04804f24a0871efc28ba52ba2025-02-07T08:21:10ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700201710.1051/itmconf/20257002017itmconf_dai2024_02017A Study on the performance of Four Regression Models in Predicting Weather Temperature Based on PythonLi Taobei0Faculty of Science and Engineering, University of Nottingham NingboFor industries like agriculture and disaster management, weather forecasting is essential. This study assesses how well four regression models—linear regression, random forest regression, support vector regression (SVR), and K-Nearest Neighbors (KNN)—predict weather temperatures using a dataset from England. Standardizing and expanding features were part of the data preprocessing process to capture non-linear interactions. Performance metrics were used to evaluate the models' predictive capacity. With the highest R2 value and the lowest error metrics, Random Forest Regression fared better than the other models, suggesting higher predictive accuracy, according to the data. KNN exhibited greater sensitivity to local fluctuations compared to SVR, which performed slightly better overall. linear Regression was the least effective, struggling with non-linear data and exhibiting higher error metrics. This study offers a thorough comparison of weather prediction regression models, emphasizing the performance of the Random Forest regression.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02017.pdf |
| spellingShingle | Li Taobei A Study on the performance of Four Regression Models in Predicting Weather Temperature Based on Python ITM Web of Conferences |
| title | A Study on the performance of Four Regression Models in Predicting Weather Temperature Based on Python |
| title_full | A Study on the performance of Four Regression Models in Predicting Weather Temperature Based on Python |
| title_fullStr | A Study on the performance of Four Regression Models in Predicting Weather Temperature Based on Python |
| title_full_unstemmed | A Study on the performance of Four Regression Models in Predicting Weather Temperature Based on Python |
| title_short | A Study on the performance of Four Regression Models in Predicting Weather Temperature Based on Python |
| title_sort | study on the performance of four regression models in predicting weather temperature based on python |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02017.pdf |
| work_keys_str_mv | AT litaobei astudyontheperformanceoffourregressionmodelsinpredictingweathertemperaturebasedonpython AT litaobei studyontheperformanceoffourregressionmodelsinpredictingweathertemperaturebasedonpython |