A Comparative Study of Data Mining Algorithms for Weather Prediction
Downloads
Accurate weather forecasting is vital for sectors such as agriculture, transportation, disaster management, and daily planning. Traditional numerical weather prediction (NWP) models rely on complex physical equations and high computational power, often facing limitations in localized or short-term forecasts. With the advancement of deep learning, data-driven models have emerged as efficient alternatives for capturing non-linear and complex temporal patterns in meteorological data. This study proposes a Convolutional Neural Network (CNN)-based approach for short-term weather prediction using structured, historical weather data. The method involves preprocessing hourly weather observations—such as temperature, humidity, wind speed, and pressure—and converting them into two-dimensional matrices suitable for CNN input. The model is trained to predict the next hour's temperature based on a 24-hour history of weather data. Experimental results show that the proposed CNN model achieves a prediction accuracy of 96.12%, outperforming traditional machine learning algorithms like Support Vector Machines (SVM) and Random Forests, as well as deep learning models such as LSTM and CNN-LSTM hybrids. The CNN architecture effectively captures both temporal and cross-feature patterns, offering a computationally efficient and accurate solution for real-time weather forecasting. Future work will explore multi-variable and multi-step forecasting, integration of satellite and spatial data, and hybrid deep learning models to further enhance performance and generalizability.
R. J. Hyndman and G. Athanasopoulos, Forecasting: principles and practice, OTexts, 2018.
T. N. Krishnamurti et al., “Improved Weather and Seasonal Climate Forecasts from Multimodel Superensemble,” Science, vol. 285, no. 5433, pp. 1548–1550, 1999.
A. Ahmad et al., “A review on machine learning forecasting techniques for weather prediction,” IEEE Access, vol. 8, pp. 123782–123795, 2020.
H. Shi, M. Xu, and R. Li, “Deep learning for weather forecasting: CNNs and LSTMs,” in Proceedings of the International Conference on Artificial Intelligence, 2018.
M. Ghimire et al., “Application of support vector machine and artificial neural network for weather prediction,” International Journal of Computer Applications, vol. 89, no. 9, pp. 13–19, 2014.
Y. Jiang and Y. Liu, “Study of random forest for weather prediction,” Procedia Engineering, vol. 24, pp. 626–632, 2011.
D. H. Lee, “Sequence-to-sequence model for weather forecasting,” Neurocomputing, vol. 459, pp. 426–435, 2021.
F. Karim et al., “Multivariate LSTM-FCNs for time series classification,” Neural Networks, vol. 116, pp. 237–245, 2019.
S. Shi, M. Gao, and Y. La, “Deep learning for precipitation nowcasting: A CNN approach,” in Proceedings of the 26th International Conference on Neural Information Processing Systems, 2015.
A. Zaytar and S. El Amrani, “Sequence to sequence weather forecasting with long short-term memory recurrent neural networks,” International Journal of Computer Applications, vol. 143, no. 11, pp. 7–11, 2016.
Z. Cui, R. Ke, and Y. Wang, “Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction,” arXiv preprint arXiv:1801.02143, 2018.
Z. Zhao, W. Chen, X. Wu, P. C. Y. Chen, and J. Liu, “LSTM network: A deep learning approach for short-term traffic forecast,” IET Intelligent Transport Systems, vol. 11, no. 2, pp. 68–75, 2017.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Advances in Neural Information Processing Systems, 2012.
N. Srivastava et al., “Dropout: A simple way to prevent neural networks from overfitting,” Journal of Machine Learning Research, vol. 15, pp. 1929–1958, 2014.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014.
J. Willmott and K. Matsuura, “Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance,” Climate Research, vol. 30, no. 1, pp. 79–82, 2005.
H. Irmanda et al., “Enhancing Weather Prediction Models through the Application of Random Forest Method and Chi-Square Feature Selection,” JOIV: International Journal on Informatics Visualization, vol. 8, no. 3, pp. 2356–2362, 2021.
B. Li and Y. Qian, “Weather Prediction Using CNN-LSTM for Time Series Analysis: A Case Study on Delhi Temperature Data,” Applied and Computational Engineering, vol. 92, pp. 121–127, 2024.
S. Kim et al., “DeepRain: ConvLSTM Network for Precipitation Prediction using Multichannel Radar Data,” arXiv preprint arXiv:1711.02316, 2017.
M. A. Ehsan et al., “Wind Speed Prediction and Visualization Using Long Short-Term Memory Networks (LSTM),” arXiv preprint arXiv:2005.12401, 2020.
