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Accurate rainfall forecasts are critical for various sectors, yet traditional methods struggle due to the complex evolving and non-linear weather patterns. This study evaluates four machine learning algorithms, Support Vector Machines (SVM), Random Forest (RF), Neural Prophet (NP), and Long Short-Term Memory (LSTM) to determine the most effective algorithm for rainfall forecasting in Zambia. Results show that Neural Prophet outperformed others, achieving the lowest RMSE (4.67), MAE (16.75), and MAPE (13.40%). Its autoregressive capabilities, interpretability, and reduced parameter complexity make Neural Prophet the preferred choice for forecasting rainfall trends in Zambia.

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A Comparative Analysis of Support Vector Machine, Random Forest, Neural Prophet, and Long Short-Term Memory Algorithms for Forecasting Rainfall in Zambia

  • 会议论文

  • 发表日期：

    2025年

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  • Lillian Mzyece,
  • Jackson Phiri,
  • Mayumbo Nyirenda

摘要

Accurate rainfall forecasts are critical for various sectors, yet traditional methods struggle due to the complex evolving and non-linear weather patterns. This study evaluates four machine learning algorithms, Support Vector Machines (SVM), Random Forest (RF), Neural Prophet (NP), and Long Short-Term Memory (LSTM) to determine the most effective algorithm for rainfall forecasting in Zambia. Results show that Neural Prophet outperformed others, achieving the lowest RMSE (4.67), MAE (16.75), and MAPE (13.40%). Its autoregressive capabilities, interpretability, and reduced parameter complexity make Neural Prophet the preferred choice for forecasting rainfall trends in Zambia.