A Model for Weather Prediction Using Hybrid Machine Learning Algorithms

Ordu; Justice; Anireh; V.I.E.; Nwiabu; N.D.

doi:10.56201/ijcsmt.vol.11.no11.2025.pg91.104

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A Model for Weather Prediction Using Hybrid Machine Learning Algorithms

Ordu, Justice, Anireh, V.I.E., Nwiabu, N.D.

Abstract

Weather prediction is crucial in agriculture, transportation, and disaster management. Accurate and timely forecasts are essential for making informed decisions and minimizing risks. This study proposes a hybrid machine learning approach for weather prediction, combining Ridge Regression for feature extraction and selection and Long Short-Term Memory (LSTM) for training and evaluation. Ridge Regression extracts and selects relevant features from input data using Python and libraries like scikit-learn, Pandas, and NumPy. The selected features are then fed into the LSTM model, a recurrent neural network (RNN) capable of capturing temporal dependencies and patterns in time series data. The hybrid model achieves superior accuracy in weather prediction compared to individual algorithms, with experimental results of 3.38% in mean absolute error, 0.2% in RMSE, and 4.7% in MAPE. The findings highlight the potential of hybrid machine learning algorithms in improving weather prediction models, contributing to better decision-making in sectors dependent on weather information. Further research can explore additional hybrid approaches and evaluate their performance on larger and more diverse datasets, paving the way for advancements in weather prediction systems.

Keywords

Weather predictionmachine learningRidge RegressionLong Short-Term Memory (LSTM)Feature extractionTemporal dependencies

References

Ashwini, M. & Jadhawar, A. (2015). Weather forecast prediction. Data Mining application, International Journal of Engineering Research and General Science, 3(2), 91-105. Askari, M. & Askari, H. (2011). Time series grey system prediction-based models: gold price forecasting. Trends in Application Science Research, 6, 1287-1292. Balogh, B., Saint-Martin, D. & Ribes, A. (2021). A toy model to investigate stability of AI-based dynamical systems. Geophysics Research Letters, 48(8). Bhardwaj, R. & Duhoon, V. (2018). Weather forecasting using soft computing techniques. In: International Conference on Computing, Power and Communication Technologies, 1111-1115. Federico, M., Luca, B. & Luciano B. (2016). On the Integration of Heterogeneous Data Sources for the Collaborative Internet of Things. In 2nd International Forum on Research and Technologies for Society and Industry (RTSI), 20-16. Haghbin, M., Sharafati, A., Motta., Al-Ansari, N. & Noghani, M. (2021) Applications of soft computing models for predicting sea surface temperature. A comprehensive review and assessment. Progress in Earth and Planetary Science, 8 (1). 4. ISSN 2197-4284 Jayasingh, S.K., Gountia, D., Samal, N. & Chinara P.K. (2021). A novel approach for data classification using neural network. IETE Journal of Research. 8, 11-24. Khajure, S. & Mohod, S. W. (2016). Future weather forecasting using soft computing techniques. Proceeding of Computer Science, 78, 402-407. Lee, J. & Lee, J. (2016) Constructing efficient regional hazardous weather prediction models through big data analysis. International Journal of Fuzzy logic and Intelligent System, 16, 1-12. Litta, A. J., Idicula, S. M. & Mohanty, U. C. (2013). Artificial neural network model in prediction of meteorological parameters during premonsoon thunderstorms. International Journal of Atmospheric Science, 14, 17-34. Mark, H., Dylan L. & Christopher, V. (2016). Machine Learning Applied to Weather Forecasting. Stanford University.2-5 Meghali, A., Kalyankar, P. & Alaspurkar, J. (2013). Data Mining Technique to Analyze the Metrological Data. International Journal of Advanced Research in Computer Science and Software Engineering, 3(2), 2277. Sani, S., Mohd, W. & Mamunu, M. (2018). A Wavelet Based Solar Radiation Prediction in Nigeria Using Adaptive Neuro-Fuzzy Approach. Indonesian Journal of Electrical Engineering and Computer Science 12(3):907-915DOI:10.11591/ijeecs.v12.i3.pp907- 915 Tian, C., Jian, M. & Chunhong, Z. (2018). A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network. Energies 11(12):3493. DOI:10.3390/en11123493 Schultz, M. G., Betancourt, C., Gong, B., Kleinert, F., Langguth, M., Leufen, L. H. Mozaffari, A. & Stadtler, S. (2021). Can deep learning beat numerical weather prediction. Phil Trans R Society A, 379, 20200097. Singh, N,, Chaturvedi, S. & Akhter, S. (2019). Weather forecasting using machine learning algorithm. In: International conference on signal processing and communication, 171-174. Sofian, I. M., Affandi, A. K., Iskandar, I. & Apriani, Y. (2018). Monthly rainfall prediction based on artificial neural networks with backpropagation and radial basis function. International Journal of Advances in Intelligent Informatics, 4(2), 154-166. Suvendra, K. J., Jibendu K. M. & Sipali P. (2022). Smart Weather Prediction Using Machine Learning. Intelligent Systems 571-583) Vathsala, H. & Koolagudi, S. (2021). Neuro-Fuzzy Model for Quantified Rainfall Prediction Using Data Mining and Soft Computing Approaches. IETE Journal of Research 69(6):1-11 DOI:10.1080/03772063.2021.1912648

A Model for Weather Prediction Using Hybrid Machine Learning Algorithms

Abstract

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References

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