Prognostication of Weather Patterns using Meteorological Data and ML Techniques




Meteorological Data, Precipitation Prediction, Radial Basis Function Neural Network, Classifier Random Forest, Gradient Boosting


In the field of modern weather prediction, the accurate classification is essential, impacting critical sectors such as agriculture, aviation, and water resource management. This research presents a weather forecasting model employing two influential classifiers random forest and technique based on gradient boosting, both implemented using the Scikit-learn library. Evaluation is based on key metrics including F1 score, accuracy, recall, and precision, with Gradient Boosting emerging as the superior choice for precipitation prediction. The study examines the performance of Random Forest Regression, Gradient Boosting Regression, and Radial Basis Function Neural Network in forecasting precipitation, drawing on prior research that demonstrated the superiority of the Random Forest algorithm in terms of accuracy and speed. Ensemble methods, particularly the Voting Classifier, a fusion of Random Forest and Gradient Boosting, outperform individual models, offering a promising avenue for advancing weather classification.


Download data is not yet available.


Hanoon, M. S., Ahmed, A. N., Zaini, N., Razzaq, A., Kumar, P., Sherif, M., Sefelnasr, A., & El-Shafie, A. Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia. Scientific Reports,2021, 11(1). DOI:

Wang, Y., Pei, L., & Wang, J., Precipitation prediction in several Chinese regions using machine learning methods. International Journal of Dynamics and Control. 2023, DOI:

Rudrappa, G., Machine Learning Models Applied for Rainfall Prediction. Revista Gestão Inovação E Tecnologias, 2021, 11(3), 179–187. DOI:

Liyew, C.M., Melese, H.A. Machine learning techniques to predict daily rainfall amount. J Big Data 8, 153, 2021, DOI:

M. Noor, I. M., Prasetyowati, S. S., & Sibaroni, Y., Prediction Map of Rainfall Classification Using Random Forest and Inverse Distance Weighted (IDW). Building of Informatics, Technology and Science, 2023 (BITS), 4(2). DOI:

Fayaz, S. A., Kaul, S., Zaman, M., & Butt, M. A. An Adaptive Gradient Boosting Model for the Prediction of Rainfall Using ID3 as a Base Estimator. Revue D’Intelligence Artificielle, 2022 36(2), 241–250. DOI:

Kundu, S., Biswas, S. K., Tripathi, D., Karmakar, R., Majumdar, S., & Mandal, S.,. A review on rainfall forecasting using ensemble learning techniques. E-Prime - Advances in Electrical Engineering, Electronics and Energy, 2023, 6, 100296. DOI:

Appiah-Badu, N. K. A., Missah, Y. M., Amekudzi, L. K., Ussiph, N., Frimpong, T., & Ahene, E., 2022, Rainfall Prediction Using Machine Learning Algorithms for the Various Ecological Zones of Ghana. IEEE Access, 10, 5069–5082. DOI:

Draper, C. S., Accounting for land model error in numerical weather prediction ensemble systems: toward ensemble-based coupled land/atmosphere data assimilation. Journal of Hydrometeorology,2022, DOI:

Song, C., & Chen, X., Performance Comparison of Machine Learning Models for Annual Precipitation Prediction Using Different Decomposition Methods. Remote Sensing, 2021, 13(5), 1018. DOI:

Barrera-Animas, A. Y., Oyedele, L. O., Bilal, M., Akinosho, T. D., Delgado, J. M. D., & Akanbi, L. A., Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting. Machine Learning With Applications, 2022 7, 100204. DOI:

Dhamodaran, S., KipsonRoy, G., Kishor, A., Refonaa, J., & JanyShabu, S. L., A Comparative Analysis of Rainfall Prediction Using Support Vector Machine and Random Forest. Journal of Computational and Theoretical Nanoscience, 2020 17(8), 3539–3542. DOI:

Hsu, K. W. On Adjustment Functions for Weight-Adjusted Voting-Based Ensembles of Classifiers. Journal of Computers, 2014, 9(7). DOI:

Chai, S. S., Wong, W. K., & Goh, K. L., Backpropagation Vs. Radial Basis Function Neural Model: Rainfall Intensity Classification For Flood Prediction Using Meteorology Data. Journal of Computer Science, 2016, 12(4), 191–200. DOI:

Gu, J., Liu, S., Zhou, Z., Chalov, S. R., & Zhuang, Q., A Stacking Ensemble Learning Model for Monthly Rainfall Prediction in the Taihu Basin, China. Water, 2022, 14(3), 492. DOI:

Ojo, O. S., & Ogunjo, S. T., Machine learning models for prediction of rainfall over Nigeria. Scientific African,2022, 16, e01246. DOI:

Balamurugan, M. S., & Manojkumar, R. , Study of short-term rain forecasting using machine learning based approach. Wireless Networks,2019 27(8), 5429–5434. DOI:

Ji, Y., Zhi, X., Ji, L., & Peng, T., Conditional Ensemble Model Output Statistics for Postprocessing of Ensemble Precipitation Forecasting. Weather and Forecasting, 2023, 38(9), 1707–1718. DOI:

Matricciani, E.. Prediction of rain attenuation in slant paths in equatorial areas: application of two layer rain model. Electronics Letters,1993, 29(1), 72–73. DOI:

Salmayenti, R., Hidayat, R., & Pramudia, A. Rainfall Prediction Using Artificial Neural Network. Agromet, 2017. 31(1), 11. DOI:




How to Cite

Mathur S, Kumar S, Choudhury T. Prognostication of Weather Patterns using Meteorological Data and ML Techniques. EAI Endorsed Trans Energy Web [Internet]. 2024 Apr. 5 [cited 2024 May 20];11. Available from: