Rainfall Forecasting in Vinh City Using Machine Learning Models

Date Received: 07-11-2025

Date Accepted: 10-06-2026

Date Published: 27-08-2026

##submissions.doi##: https://doi.org/10.31817/tckhnnvn.2026.24.8.08

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How to Cite:

Hai, N., Dung, P., Quynh, T., & Thao, N. (2026). Rainfall Forecasting in Vinh City Using Machine Learning Models. Vietnam Journal of Agricultural Sciences, 24(8), 1097–1107. https://doi.org/10.31817/tckhnnvn.2026.24.8.08

Rainfall Forecasting in Vinh City Using Machine Learning Models

Nguyen Huu Hai (*) , Pham Quang Dung , Tran Duc Quynh , Nguyen Thi Thao

  • Tác giả liên hệ: [email protected]

    • Keywords

    Rainfall prediction, machine learning, feature extraction, feature selection, time series data, artificial intelligence methods

    Abstract


    Rainfall is a vital climatic factor that directly influences natural ecosystems, human life, and particularly agricultural production. In the context of increasingly complex climate change, with the growing frequency of extreme weather events such as heavy rainfall, storms, and floods, accurate rainfall prediction has become both crucial and challenging. This study focuses on evaluating the performance of various machine learning models in forecasting rainfall in a specific region. Rainfall data are typically characterized by strong nonlinearity, high variability, considerable randomness, and substantial noise. Therefore, this research proposes a multi-step data enhancement approach to improve model accuracy. Several machine learning algorithms are implemented and evaluated using key performance metrics such as Accuracy, Weighted F1-score, and AUC. The results indicate that the forecasting models achieved substantial performance improvements when trained on new dataset. Among them, XGBoost exhibited the most notable gains, with an average increase of 15.5% in both accuracy and weighted F1-score compared with using only the original data. The findings demonstrate the potential applicability of the proposed approach in regional rainfall analysis and forecasting, contributing to more reliable climate prediction and decision support, especially in agricultural production and water resources management.

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