Temperature and Humidity Prediction Based on Machine Learning
The growing impact of global climate change, emphasizing the critical importance of accurately predicting weather conditions, particularly temperature and humidity. These predictions are crucial for key sectors such as agriculture, energy management, and public safety. This paper employs various mac...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
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_04004.pdf |
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| Summary: | The growing impact of global climate change, emphasizing the critical importance of accurately predicting weather conditions, particularly temperature and humidity. These predictions are crucial for key sectors such as agriculture, energy management, and public safety. This paper employs various machine learning models, including Linear Regression(LR). Support Vector Machine(SVM), Neural Network(NN), and Random Forest(RF). to analyze their accuracy in predicting temperature and humidity. The results indicate that the NN model outperforms the others, showing excellent performance in the dataset. Li addition to the outstanding performance of the neural NN. the RF and SVM also demonstrated strong performance, particularly hi handling specific features within the dataset, the model's performance can be further optimized by adjusting the NN's hyperparameters or introducing more feature engineering, which could lead to even better results hi future data analyses. This research highlights the significant potential of machine learning techniques hi enhancing meteorological forecasting, providing valuable insights and tools for improving decision-making in industries heavily influenced by weather conditions. |
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| ISSN: | 2271-2097 |