Abstract
Aim/Background
The study highlights the significance of rainfall in Earth's water cycle and its impact on ecosystems and human activities. Stating why unusual rainfall conditions can threaten food security and livelihoods, necessitating early warning systems for adaptation. It explores the historical evolution of weather prediction efforts from some ancient theories to modern machine learning (ML) techniques. This study then compares four supervised machine learning algorithms, Random Forest (RF), Long Short-Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), and Linear Regression (LR), to forecast daily rainfall amounts across 49 Australian cities using a 2007–2017 dataset downloaded in 2022.
Methods
Missing data were addressed through a hybrid procedure combining a custom algorithm with K-Nearest-Neighbor imputation, and mode imputation for categorical fields. Model performance was evaluated using MAE, MSE, and RMSE over forecast horizons (1, 5, 10, 15, 20, 25, and 30-days interval) and compared to a naïve zero-rain baseline. Categorical inputs for neural models were converted to learned embeddings; LR used feature selection informed by Pearson correlation and predictors were scaled where appropriate; RF training omitted scaling; ARIMA was trained and validated using walk-forward validation on a single long station record; ML models were trained across multiple stations with an 80/20 train/test split.
Results
Results show that the zero baseline is a strong MAE benchmark due to heavy zero-rain inflation (median rainfall is 0 mm); no single learned model outperformed it on MAE across all horizons. ARIMA consistently reduced squared-error metrics (MSE & RMSE) and per-formed competitively on MAE, indicating its strength at capturing large events and short-term temporal structure. RF and LR produced moderate errors overall, while LSTM yielded competitive MAE but substantially larger MSE/RMSE – suggesting occasional large outliers. Errors are geographically and seasonally heterogeneous (largest in Dec–Mar and at tropical stations such as Darwin). Results are partially affected by preprocessing and validation differences (notably ARIMA’s single-station training versus ML models trained on multiple stations).
Conclusion
Forecasting daily rainfall from routine meteorological predictors on this multi-location dataset is challenging because of zero-inflation and spatial heterogeneity. A single-stage regression that only minimizes MAE is insufficient for operational use. Recommended next steps include two-stage occurrence + amount modeling, consistent per-location temporal splits for fair model comparison, climate-zone or location-specific models to better capture local patterns, and conditional evaluation on wet days and extremes.

Key words: Rain Prediction, Random Forest, ARIMA, LSTM, Linear Regression, Machine Learning, Missing values